2025-10-28 12:32:50.713364: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-28 12:32:50.724543: 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 00:00:1761651170.738498 1646624 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761651170.742839 1646624 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761651170.753231 1646624 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761651170.753255 1646624 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761651170.753260 1646624 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761651170.753263 1646624 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-28 12:32:50.756550: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/ray/tune/impl/tuner_internal.py:144: RayDeprecationWarning: The `RunConfig` class should be imported from `ray.tune` when passing it to the Tuner. Please update your imports. See this issue for more context and migration options: https://github.com/ray-project/ray/issues/49454. Disable these warnings by setting the environment variable: RAY_TRAIN_ENABLE_V2_MIGRATION_WARNINGS=0
  _log_deprecation_warning(
2025-10-28 12:32:53,939	INFO worker.py:1927 -- Started a local Ray instance.
2025-10-28 12:32:54,624	INFO tune.py:253 -- Initializing Ray automatically. For cluster usage or custom Ray initialization, call `ray.init(...)` before `Tuner(...)`.
2025-10-28 12:32:54,697	INFO trial.py:182 -- Creating a new dirname dir_d8531_ee8c because trial dirname 'dir_d8531' already exists.
2025-10-28 12:32:54,701	INFO trial.py:182 -- Creating a new dirname dir_d8531_ee99 because trial dirname 'dir_d8531' already exists.
2025-10-28 12:32:54,706	INFO trial.py:182 -- Creating a new dirname dir_d8531_3a9f because trial dirname 'dir_d8531' already exists.
2025-10-28 12:32:54,710	INFO trial.py:182 -- Creating a new dirname dir_d8531_a320 because trial dirname 'dir_d8531' already exists.
2025-10-28 12:32:54,712	INFO trial.py:182 -- Creating a new dirname dir_d8531_cdc5 because trial dirname 'dir_d8531' already exists.
2025-10-28 12:32:54,714	INFO trial.py:182 -- Creating a new dirname dir_d8531_3264 because trial dirname 'dir_d8531' already exists.
2025-10-28 12:32:54,717	INFO trial.py:182 -- Creating a new dirname dir_d8531_e58d because trial dirname 'dir_d8531' already exists.
2025-10-28 12:32:54,720	INFO trial.py:182 -- Creating a new dirname dir_d8531_da87 because trial dirname 'dir_d8531' already exists.
2025-10-28 12:32:54,722	INFO trial.py:182 -- Creating a new dirname dir_d8531_d605 because trial dirname 'dir_d8531' already exists.
2025-10-28 12:32:54,725	INFO trial.py:182 -- Creating a new dirname dir_d8531_986e because trial dirname 'dir_d8531' already exists.
2025-10-28 12:32:54,729	INFO trial.py:182 -- Creating a new dirname dir_d8531_42dc because trial dirname 'dir_d8531' already exists.
2025-10-28 12:32:54,731	INFO trial.py:182 -- Creating a new dirname dir_d8531_a98c because trial dirname 'dir_d8531' already exists.
2025-10-28 12:32:54,734	INFO trial.py:182 -- Creating a new dirname dir_d8531_1bca because trial dirname 'dir_d8531' already exists.
2025-10-28 12:32:54,737	INFO trial.py:182 -- Creating a new dirname dir_d8531_541b because trial dirname 'dir_d8531' already exists.
2025-10-28 12:32:54,740	INFO trial.py:182 -- Creating a new dirname dir_d8531_c7b6 because trial dirname 'dir_d8531' already exists.
2025-10-28 12:32:54,744	INFO trial.py:182 -- Creating a new dirname dir_d8531_f5c1 because trial dirname 'dir_d8531' already exists.
2025-10-28 12:32:54,747	INFO trial.py:182 -- Creating a new dirname dir_d8531_cbc7 because trial dirname 'dir_d8531' already exists.
2025-10-28 12:32:54,751	INFO trial.py:182 -- Creating a new dirname dir_d8531_4fd0 because trial dirname 'dir_d8531' already exists.
2025-10-28 12:32:54,756	INFO trial.py:182 -- Creating a new dirname dir_d8531_6620 because trial dirname 'dir_d8531' already exists.
1 GPU(s) detected and VRAM set to crossover mode..
Se lanza la búsqueda de hiperparámetros óptimos del modelo
╭─────────────────────────────────────────────────────────────────╮
│ Configuration for experiment     ESANN_hyperparameters_tuning   │
├─────────────────────────────────────────────────────────────────┤
│ Search algorithm                 BasicVariantGenerator          │
│ Scheduler                        AsyncHyperBandScheduler        │
│ Number of trials                 20                             │
╰─────────────────────────────────────────────────────────────────╯

View detailed results here: /mnt/nvme1n2/git/uniovi-simur-wearablepermed-data/output/Paper_results/cases_dataset_M/case_M_ESANN_acc_gyr_17_classes/ESANN_hyperparameters_tuning
To visualize your results with TensorBoard, run: `tensorboard --logdir /tmp/ray/session_2025-10-28_12-32-53_222796_1646624/artifacts/2025-10-28_12-32-54/ESANN_hyperparameters_tuning/driver_artifacts`

Trial status: 20 PENDING
Current time: 2025-10-28 12:32:54. Total running time: 0s
Logical resource usage: 15.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status       N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs │
├───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_d8531    PENDING            3   rmsprop         tanh                                   32                 64                  5          9.38039e-05         66 │
│ trial_d8531    PENDING            4   rmsprop         tanh                                   32                128                  5          0.00287631          87 │
│ trial_d8531    PENDING            3   adam            tanh                                  128                128                  5          9.43275e-05         83 │
│ trial_d8531    PENDING            3   adam            tanh                                  128                 64                  5          0.00473849          83 │
│ trial_d8531    PENDING            4   rmsprop         tanh                                  128                 32                  3          2.75956e-05        127 │
│ trial_d8531    PENDING            2   adam            tanh                                   32                 32                  3          5.99053e-05         93 │
│ trial_d8531    PENDING            3   adam            tanh                                   32                 64                  5          2.55695e-05         54 │
│ trial_d8531    PENDING            3   adam            tanh                                   32                128                  5          0.00225539          99 │
│ trial_d8531    PENDING            2   adam            tanh                                  128                128                  3          6.55104e-05         81 │
│ trial_d8531    PENDING            3   rmsprop         relu                                   32                 64                  3          0.000151011         91 │
│ trial_d8531    PENDING            2   rmsprop         relu                                   64                 64                  3          0.00308065         106 │
│ trial_d8531    PENDING            4   adam            relu                                  128                128                  5          0.000175915        124 │
│ trial_d8531    PENDING            4   adam            relu                                  128                 32                  3          1.96033e-05        102 │
│ trial_d8531    PENDING            4   rmsprop         tanh                                  128                128                  5          0.00244001         129 │
│ trial_d8531    PENDING            3   adam            tanh                                   32                 64                  5          0.00455572          99 │
│ trial_d8531    PENDING            3   adam            tanh                                   32                 64                  5          0.00361478         114 │
│ trial_d8531    PENDING            4   rmsprop         relu                                   32                 32                  5          0.00235051         122 │
│ trial_d8531    PENDING            3   rmsprop         relu                                   64                 32                  3          9.27852e-05         92 │
│ trial_d8531    PENDING            4   adam            relu                                  128                 32                  3          8.42402e-05         67 │
│ trial_d8531    PENDING            2   rmsprop         tanh                                   64                 32                  5          4.95152e-05         85 │
╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

Trial trial_d8531 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_d8531 config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                            93 │
│ funcion_activacion              tanh │
│ numero_filtros                    32 │
│ optimizador                     adam │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00006 │
╰──────────────────────────────────────╯
Trial trial_d8531 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_d8531 config             │
├──────────────────────────────────────┤
│ N_capas                            3 │
│ epochs                            99 │
│ funcion_activacion              tanh │
│ numero_filtros                   128 │
│ optimizador                     adam │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00226 │
╰──────────────────────────────────────╯
Trial trial_d8531 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_d8531 config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                           106 │
│ funcion_activacion              relu │
│ numero_filtros                    64 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  64 │
│ tasa_aprendizaje             0.00308 │
╰──────────────────────────────────────╯
Trial trial_d8531 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_d8531 config             │
├──────────────────────────────────────┤
│ N_capas                            3 │
│ epochs                            91 │
│ funcion_activacion              relu │
│ numero_filtros                    64 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00015 │
╰──────────────────────────────────────╯
Trial trial_d8531 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_d8531 config             │
├──────────────────────────────────────┤
│ N_capas                            3 │
│ epochs                            54 │
│ funcion_activacion              tanh │
│ numero_filtros                    64 │
│ optimizador                     adam │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00003 │
╰──────────────────────────────────────╯
Trial trial_d8531 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_d8531 config             │
├──────────────────────────────────────┤
│ N_capas                            4 │
│ epochs                           129 │
│ funcion_activacion              tanh │
│ numero_filtros                   128 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     5 │
│ tamanho_minilote                 128 │
│ tasa_aprendizaje             0.00244 │
╰──────────────────────────────────────╯
Trial trial_d8531 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_d8531 config             │
├──────────────────────────────────────┤
│ N_capas                            3 │
│ epochs                            66 │
│ funcion_activacion              tanh │
│ numero_filtros                    64 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00009 │
╰──────────────────────────────────────╯
Trial trial_d8531 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_d8531 config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                            85 │
│ funcion_activacion              tanh │
│ numero_filtros                    32 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  64 │
│ tasa_aprendizaje             0.00005 │
╰──────────────────────────────────────╯
Trial trial_d8531 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_d8531 config             │
├──────────────────────────────────────┤
│ N_capas                            4 │
│ epochs                           127 │
│ funcion_activacion              tanh │
│ numero_filtros                    32 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     3 │
│ tamanho_minilote                 128 │
│ tasa_aprendizaje             0.00003 │
╰──────────────────────────────────────╯
Trial trial_d8531 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_d8531 config             │
├──────────────────────────────────────┤
│ N_capas                            3 │
│ epochs                           114 │
│ funcion_activacion              tanh │
│ numero_filtros                    64 │
│ optimizador                     adam │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00361 │
╰──────────────────────────────────────╯
Trial trial_d8531 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_d8531 config             │
├──────────────────────────────────────┤
│ N_capas                            4 │
│ epochs                           122 │
│ funcion_activacion              relu │
│ numero_filtros                    32 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00235 │
╰──────────────────────────────────────╯
Trial trial_d8531 started with configuration:
[36m(train_cnn_ray_tune pid=1648205)[0m 2025-10-28 12:32:57.940958: 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=1648205)[0m 2025-10-28 12:32:57.962150: 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=1648205)[0m WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
[36m(train_cnn_ray_tune pid=1648205)[0m E0000 00:00:1761651177.991755 1649404 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=1648205)[0m E0000 00:00:1761651177.999832 1649404 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=1648205)[0m W0000 00:00:1761651178.019975 1649404 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=1648205)[0m W0000 00:00:1761651178.020011 1649404 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=1648205)[0m W0000 00:00:1761651178.020014 1649404 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=1648205)[0m W0000 00:00:1761651178.020017 1649404 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=1648205)[0m 2025-10-28 12:32:58.026075: 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=1648205)[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=1648205)[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=1648205)[0m   warnings.warn(
[36m(train_cnn_ray_tune pid=1648205)[0m 2025-10-28 12:33:01.379327: 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=1648205)[0m 2025-10-28 12:33:01.379383: I external/local_xla/xla/stream_executor/cuda/cuda_diagnostics.cc:167] env: CUDA_VISIBLE_DEVICES=""
[36m(train_cnn_ray_tune pid=1648205)[0m 2025-10-28 12:33:01.379393: 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=1648205)[0m 2025-10-28 12:33:01.379398: 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=1648205)[0m 2025-10-28 12:33:01.379404: 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=1648205)[0m 2025-10-28 12:33:01.379408: I external/local_xla/xla/stream_executor/cuda/cuda_diagnostics.cc:190] hostname: simur-MS-7B94
[36m(train_cnn_ray_tune pid=1648205)[0m 2025-10-28 12:33:01.379659: 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=1648205)[0m 2025-10-28 12:33:01.379701: 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=1648205)[0m 2025-10-28 12:33:01.379707: I external/local_xla/xla/stream_executor/cuda/cuda_diagnostics.cc:291] kernel version seems to match DSO: 570.133.7
╭──────────────────────────────────────╮
│ Trial trial_d8531 config             │
├──────────────────────────────────────┤
│ N_capas                            3 │
│ epochs                            83 │
│ funcion_activacion              tanh │
│ numero_filtros                    64 │
│ optimizador                     adam │
│ tamanho_filtro                     5 │
│ tamanho_minilote                 128 │
│ tasa_aprendizaje             0.00474 │
╰──────────────────────────────────────╯
Trial trial_d8531 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_d8531 config             │
├──────────────────────────────────────┤
│ N_capas                            3 │
│ epochs                            99 │
│ funcion_activacion              tanh │
│ numero_filtros                    64 │
│ optimizador                     adam │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00456 │
╰──────────────────────────────────────╯
Trial trial_d8531 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_d8531 config             │
├──────────────────────────────────────┤
│ N_capas                            3 │
│ epochs                            83 │
│ funcion_activacion              tanh │
│ numero_filtros                   128 │
│ optimizador                     adam │
│ tamanho_filtro                     5 │
│ tamanho_minilote                 128 │
│ tasa_aprendizaje             0.00009 │
╰──────────────────────────────────────╯
Trial trial_d8531 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_d8531 config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                            81 │
│ funcion_activacion              tanh │
│ numero_filtros                   128 │
│ optimizador                     adam │
│ tamanho_filtro                     3 │
│ tamanho_minilote                 128 │
│ tasa_aprendizaje             0.00007 │
╰──────────────────────────────────────╯
Trial trial_d8531 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_d8531 config             │
├──────────────────────────────────────┤
│ N_capas                            4 │
│ epochs                            87 │
│ funcion_activacion              tanh │
│ numero_filtros                   128 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00288 │
╰──────────────────────────────────────╯
Trial trial_d8531 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_d8531 config             │
├──────────────────────────────────────┤
│ N_capas                            4 │
│ epochs                           102 │
│ funcion_activacion              relu │
│ numero_filtros                    32 │
│ optimizador                     adam │
│ tamanho_filtro                     3 │
│ tamanho_minilote                 128 │
│ tasa_aprendizaje             0.00002 │
╰──────────────────────────────────────╯
Trial trial_d8531 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_d8531 config             │
├──────────────────────────────────────┤
│ N_capas                            4 │
│ epochs                           124 │
│ funcion_activacion              relu │
│ numero_filtros                   128 │
│ optimizador                     adam │
│ tamanho_filtro                     5 │
│ tamanho_minilote                 128 │
│ tasa_aprendizaje             0.00018 │
╰──────────────────────────────────────╯
Trial trial_d8531 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_d8531 config             │
├──────────────────────────────────────┤
│ N_capas                            4 │
│ epochs                            67 │
│ funcion_activacion              relu │
│ numero_filtros                    32 │
│ optimizador                     adam │
│ tamanho_filtro                     3 │
│ tamanho_minilote                 128 │
│ tasa_aprendizaje             0.00008 │
╰──────────────────────────────────────╯
Trial trial_d8531 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_d8531 config             │
├──────────────────────────────────────┤
│ N_capas                            3 │
│ epochs                            92 │
│ funcion_activacion              relu │
│ numero_filtros                    32 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  64 │
│ tasa_aprendizaje             0.00009 │
╰──────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=1648205)[0m Model: "sequential"
[36m(train_cnn_ray_tune pid=1648205)[0m ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
[36m(train_cnn_ray_tune pid=1648205)[0m ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
[36m(train_cnn_ray_tune pid=1648205)[0m ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
[36m(train_cnn_ray_tune pid=1648205)[0m │ conv1d (Conv1D)                 │ (None, 6, 32)          │        24,032 │
[36m(train_cnn_ray_tune pid=1648205)[0m ├─────────────────────────────────┼────────────────────────┼───────────────┤
[36m(train_cnn_ray_tune pid=1648205)[0m │ layer_normalization             │ (None, 6, 32)          │            64 │
[36m(train_cnn_ray_tune pid=1648205)[0m │ (LayerNormalization)            │                        │               │
[36m(train_cnn_ray_tune pid=1648205)[0m ├─────────────────────────────────┼────────────────────────┼───────────────┤
[36m(train_cnn_ray_tune pid=1648205)[0m │ dropout (Dropout)               │ (None, 6, 32)          │             0 │
[36m(train_cnn_ray_tune pid=1648205)[0m ├─────────────────────────────────┼────────────────────────┼───────────────┤
[36m(train_cnn_ray_tune pid=1648205)[0m │ conv1d_1 (Conv1D)               │ (None, 6, 32)          │         3,104 │
[36m(train_cnn_ray_tune pid=1648205)[0m ├─────────────────────────────────┼────────────────────────┼───────────────┤
[36m(train_cnn_ray_tune pid=1648205)[0m │ layer_normalization_1           │ (None, 6, 32)          │            64 │
[36m(train_cnn_ray_tune pid=1648205)[0m │ (LayerNormalization)            │                        │               │
[36m(train_cnn_ray_tune pid=1648205)[0m ├─────────────────────────────────┼────────────────────────┼───────────────┤
[36m(train_cnn_ray_tune pid=1648205)[0m │ dropout_1 (Dropout)             │ (None, 6, 32)          │             0 │
[36m(train_cnn_ray_tune pid=1648205)[0m ├─────────────────────────────────┼────────────────────────┼───────────────┤
[36m(train_cnn_ray_tune pid=1648205)[0m │ global_average_pooling1d        │ (None, 32)             │             0 │
[36m(train_cnn_ray_tune pid=1648205)[0m │ (GlobalAveragePooling1D)        │                        │               │
[36m(train_cnn_ray_tune pid=1648205)[0m ├─────────────────────────────────┼────────────────────────┼───────────────┤
[36m(train_cnn_ray_tune pid=1648205)[0m │ dropout_2 (Dropout)             │ (None, 32)             │             0 │
[36m(train_cnn_ray_tune pid=1648205)[0m ├─────────────────────────────────┼────────────────────────┼───────────────┤
[36m(train_cnn_ray_tune pid=1648205)[0m │ dense (Dense)                   │ (None, 15)             │           495 │
[36m(train_cnn_ray_tune pid=1648205)[0m └─────────────────────────────────┴────────────────────────┴───────────────┘
[36m(train_cnn_ray_tune pid=1648205)[0m  Total params: 27,759 (108.43 KB)
[36m(train_cnn_ray_tune pid=1648205)[0m  Trainable params: 27,759 (108.43 KB)
[36m(train_cnn_ray_tune pid=1648205)[0m  Non-trainable params: 0 (0.00 B)
[36m(train_cnn_ray_tune pid=1648239)[0m  Total params: 326,927 (1.25 MB)
[36m(train_cnn_ray_tune pid=1648239)[0m  Trainable params: 326,927 (1.25 MB)
[36m(train_cnn_ray_tune pid=1648205)[0m Epoch 1/93
[36m(train_cnn_ray_tune pid=1648246)[0m 
[1m  1/289[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12:29[0m 3s/step - accuracy: 0.0938 - loss: 3.2190
[36m(train_cnn_ray_tune pid=1648203)[0m 
[1m  1/289[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12:45[0m 3s/step - accuracy: 0.0469 - loss: 3.0002
[1m  4/289[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 19ms/step - accuracy: 0.0485 - loss: 3.0759 
[36m(train_cnn_ray_tune pid=1648246)[0m 
[1m  5/289[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 17ms/step - accuracy: 0.0812 - loss: 3.1481 
[1m  9/289[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 17ms/step - accuracy: 0.0848 - loss: 3.1261
[36m(train_cnn_ray_tune pid=1648203)[0m 
[1m  8/289[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 16ms/step - accuracy: 0.0513 - loss: 3.0912
[1m 12/289[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 15ms/step - accuracy: 0.0521 - loss: 3.1001
[36m(train_cnn_ray_tune pid=1648246)[0m 
[1m 12/289[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 17ms/step - accuracy: 0.0851 - loss: 3.1097
[36m(train_cnn_ray_tune pid=1648203)[0m 
[1m 16/289[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 15ms/step - accuracy: 0.0545 - loss: 3.0965
[1m 20/289[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 15ms/step - accuracy: 0.0563 - loss: 3.0927
[36m(train_cnn_ray_tune pid=1648246)[0m 
[1m 16/289[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 16ms/step - accuracy: 0.0860 - loss: 3.0919
[36m(train_cnn_ray_tune pid=1648246)[0m 
[1m 19/289[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 17ms/step - accuracy: 0.0866 - loss: 3.0804
[36m(train_cnn_ray_tune pid=1648203)[0m 
[1m 24/289[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 14ms/step - accuracy: 0.0580 - loss: 3.0882
[1m 28/289[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 14ms/step - accuracy: 0.0596 - loss: 3.0833
[36m(train_cnn_ray_tune pid=1648194)[0m 
[1m  4/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 24ms/step - accuracy: 0.0872 - loss: 3.2315
[1m  7/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.0907 - loss: 3.2108
[36m(train_cnn_ray_tune pid=1648194)[0m 
[1m 10/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.0933 - loss: 3.2039
[1m 14/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 19ms/step - accuracy: 0.0922 - loss: 3.1980
[36m(train_cnn_ray_tune pid=1648194)[0m 
[1m 17/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.0917 - loss: 3.1910
[36m(train_cnn_ray_tune pid=1648194)[0m 
[1m 19/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 21ms/step - accuracy: 0.0913 - loss: 3.1865
[1m 22/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 21ms/step - accuracy: 0.0909 - loss: 3.1808
[36m(train_cnn_ray_tune pid=1648194)[0m 
[1m 24/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.0907 - loss: 3.1771
[1m 27/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 22ms/step - accuracy: 0.0906 - loss: 3.1707
[36m(train_cnn_ray_tune pid=1648194)[0m 
[1m 42/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 22ms/step - accuracy: 0.0878 - loss: 3.1552
[36m(train_cnn_ray_tune pid=1648194)[0m 
[1m 45/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 22ms/step - accuracy: 0.0871 - loss: 3.1537
[36m(train_cnn_ray_tune pid=1648239)[0m 
[1m  3/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m21s[0m 38ms/step - accuracy: 0.0260 - loss: 3.2763    
[1m  5/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m22s[0m 39ms/step - accuracy: 0.0428 - loss: 3.2161
[36m(train_cnn_ray_tune pid=1648194)[0m 
[1m 48/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 21ms/step - accuracy: 0.0865 - loss: 3.1523
[36m(train_cnn_ray_tune pid=1648212)[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=1648212)[0m ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=1648212)[0m ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=1648212)[0m ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=1648212)[0m │ global_average_pooling1d        │ (None, 128)            │             0 │[32m [repeated 122x across cluster][0m
[36m(train_cnn_ray_tune pid=1648212)[0m ├─────────────────────────────────┼────────────────────────┼───────────────┤[32m [repeated 221x across cluster][0m
[36m(train_cnn_ray_tune pid=1648212)[0m │ layer_normalization             │ (None, 6, 128)         │           256 │[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=1648212)[0m │ (LayerNormalization)            │                        │               │[32m [repeated 61x across cluster][0m
[36m(train_cnn_ray_tune pid=1648212)[0m │ dropout (Dropout)               │ (None, 6, 128)         │             0 │[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=1648212)[0m │ dropout_4 (Dropout)             │ (None, 128)            │             0 │[32m [repeated 61x across cluster][0m
[36m(train_cnn_ray_tune pid=1648212)[0m │ (GlobalAveragePooling1D)        │                        │               │[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=1648212)[0m │ dense (Dense)                   │ (None, 15)             │         1,935 │[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=1648212)[0m └─────────────────────────────────┴────────────────────────┴───────────────┘[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=1648250)[0m  Total params: 34,095 (133.18 KB)[32m [repeated 14x across cluster][0m
[36m(train_cnn_ray_tune pid=1648250)[0m  Trainable params: 34,095 (133.18 KB)[32m [repeated 14x across cluster][0m
[36m(train_cnn_ray_tune pid=1648212)[0m  Non-trainable params: 0 (0.00 B)[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=1648212)[0m  Total params: 409,231 (1.56 MB)[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=1648212)[0m  Trainable params: 409,231 (1.56 MB)[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=1648250)[0m Epoch 1/102[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=1648249)[0m 
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[36m(train_cnn_ray_tune pid=1648249)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648249)[0m 
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[36m(train_cnn_ray_tune pid=1648249)[0m 
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[36m(train_cnn_ray_tune pid=1648249)[0m 
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[36m(train_cnn_ray_tune pid=1648249)[0m 
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[36m(train_cnn_ray_tune pid=1648249)[0m 
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[36m(train_cnn_ray_tune pid=1648240)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648250)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648204)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648248)[0m 
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[36m(train_cnn_ray_tune pid=1648202)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648203)[0m 
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[36m(train_cnn_ray_tune pid=1648203)[0m Epoch 2/85
[36m(train_cnn_ray_tune pid=1648246)[0m 
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[36m(train_cnn_ray_tune pid=1648248)[0m 
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[36m(train_cnn_ray_tune pid=1648246)[0m 
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[36m(train_cnn_ray_tune pid=1648246)[0m 
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[36m(train_cnn_ray_tune pid=1648203)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648246)[0m 
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[36m(train_cnn_ray_tune pid=1648251)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648240)[0m 
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[36m(train_cnn_ray_tune pid=1648240)[0m Epoch 2/92[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=1648227)[0m 
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[36m(train_cnn_ray_tune pid=1648251)[0m 
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[36m(train_cnn_ray_tune pid=1648221)[0m 
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[36m(train_cnn_ray_tune pid=1648204)[0m 
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[36m(train_cnn_ray_tune pid=1648194)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648252)[0m 
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[36m(train_cnn_ray_tune pid=1648251)[0m 
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[36m(train_cnn_ray_tune pid=1648240)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648204)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648203)[0m 
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[36m(train_cnn_ray_tune pid=1648203)[0m Epoch 3/85[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=1648250)[0m 
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[36m(train_cnn_ray_tune pid=1648203)[0m 
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[36m(train_cnn_ray_tune pid=1648203)[0m 
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Trial status: 20 RUNNING
Current time: 2025-10-28 12:33:24. 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_d8531    RUNNING            3   rmsprop         tanh                                   32                 64                  5          9.38039e-05         66 │
│ trial_d8531    RUNNING            4   rmsprop         tanh                                   32                128                  5          0.00287631          87 │
│ trial_d8531    RUNNING            3   adam            tanh                                  128                128                  5          9.43275e-05         83 │
│ trial_d8531    RUNNING            3   adam            tanh                                  128                 64                  5          0.00473849          83 │
│ trial_d8531    RUNNING            4   rmsprop         tanh                                  128                 32                  3          2.75956e-05        127 │
│ trial_d8531    RUNNING            2   adam            tanh                                   32                 32                  3          5.99053e-05         93 │
│ trial_d8531    RUNNING            3   adam            tanh                                   32                 64                  5          2.55695e-05         54 │
│ trial_d8531    RUNNING            3   adam            tanh                                   32                128                  5          0.00225539          99 │
│ trial_d8531    RUNNING            2   adam            tanh                                  128                128                  3          6.55104e-05         81 │
│ trial_d8531    RUNNING            3   rmsprop         relu                                   32                 64                  3          0.000151011         91 │
│ trial_d8531    RUNNING            2   rmsprop         relu                                   64                 64                  3          0.00308065         106 │
│ trial_d8531    RUNNING            4   adam            relu                                  128                128                  5          0.000175915        124 │
│ trial_d8531    RUNNING            4   adam            relu                                  128                 32                  3          1.96033e-05        102 │
│ trial_d8531    RUNNING            4   rmsprop         tanh                                  128                128                  5          0.00244001         129 │
│ trial_d8531    RUNNING            3   adam            tanh                                   32                 64                  5          0.00455572          99 │
│ trial_d8531    RUNNING            3   adam            tanh                                   32                 64                  5          0.00361478         114 │
│ trial_d8531    RUNNING            4   rmsprop         relu                                   32                 32                  5          0.00235051         122 │
│ trial_d8531    RUNNING            3   rmsprop         relu                                   64                 32                  3          9.27852e-05         92 │
│ trial_d8531    RUNNING            4   adam            relu                                  128                 32                  3          8.42402e-05         67 │
│ trial_d8531    RUNNING            2   rmsprop         tanh                                   64                 32                  5          4.95152e-05         85 │
╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648240)[0m 
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[36m(train_cnn_ray_tune pid=1648246)[0m 
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[36m(train_cnn_ray_tune pid=1648239)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648203)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m Epoch 3/127[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648250)[0m 
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[36m(train_cnn_ray_tune pid=1648240)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648227)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648249)[0m 
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[36m(train_cnn_ray_tune pid=1648246)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648238)[0m 
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[36m(train_cnn_ray_tune pid=1648203)[0m 
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[36m(train_cnn_ray_tune pid=1648249)[0m 
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[36m(train_cnn_ray_tune pid=1648203)[0m 
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[36m(train_cnn_ray_tune pid=1648203)[0m Epoch 4/85[32m [repeated 12x across cluster][0m
[36m(train_cnn_ray_tune pid=1648251)[0m 
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[36m(train_cnn_ray_tune pid=1648194)[0m 
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[36m(train_cnn_ray_tune pid=1648194)[0m 
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[36m(train_cnn_ray_tune pid=1648204)[0m 
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[36m(train_cnn_ray_tune pid=1648227)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648203)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648221)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648204)[0m 
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[36m(train_cnn_ray_tune pid=1648204)[0m Epoch 4/83[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=1648204)[0m 
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[36m(train_cnn_ray_tune pid=1648203)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648204)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648202)[0m 
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[36m(train_cnn_ray_tune pid=1648251)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648238)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648203)[0m 
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[36m(train_cnn_ray_tune pid=1648227)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m Epoch 3/83[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=1648202)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648239)[0m 
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[36m(train_cnn_ray_tune pid=1648239)[0m 
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[36m(train_cnn_ray_tune pid=1648251)[0m 
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[36m(train_cnn_ray_tune pid=1648252)[0m 
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[36m(train_cnn_ray_tune pid=1648204)[0m 
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[36m(train_cnn_ray_tune pid=1648202)[0m 
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[36m(train_cnn_ray_tune pid=1648202)[0m 
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[36m(train_cnn_ray_tune pid=1648202)[0m Epoch 2/87[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=1648249)[0m 
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[36m(train_cnn_ray_tune pid=1648246)[0m 
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[36m(train_cnn_ray_tune pid=1648227)[0m 
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[36m(train_cnn_ray_tune pid=1648251)[0m 
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[36m(train_cnn_ray_tune pid=1648251)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648202)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648240)[0m 
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[36m(train_cnn_ray_tune pid=1648203)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m Epoch 6/127[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648240)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648238)[0m 
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[36m(train_cnn_ray_tune pid=1648248)[0m 
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Trial status: 20 RUNNING
Current time: 2025-10-28 12:33:55. Total running time: 1min 0s
Logical resource usage: 20.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status       N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs │
├───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_d8531    RUNNING            3   rmsprop         tanh                                   32                 64                  5          9.38039e-05         66 │
│ trial_d8531    RUNNING            4   rmsprop         tanh                                   32                128                  5          0.00287631          87 │
│ trial_d8531    RUNNING            3   adam            tanh                                  128                128                  5          9.43275e-05         83 │
│ trial_d8531    RUNNING            3   adam            tanh                                  128                 64                  5          0.00473849          83 │
│ trial_d8531    RUNNING            4   rmsprop         tanh                                  128                 32                  3          2.75956e-05        127 │
│ trial_d8531    RUNNING            2   adam            tanh                                   32                 32                  3          5.99053e-05         93 │
│ trial_d8531    RUNNING            3   adam            tanh                                   32                 64                  5          2.55695e-05         54 │
│ trial_d8531    RUNNING            3   adam            tanh                                   32                128                  5          0.00225539          99 │
│ trial_d8531    RUNNING            2   adam            tanh                                  128                128                  3          6.55104e-05         81 │
│ trial_d8531    RUNNING            3   rmsprop         relu                                   32                 64                  3          0.000151011         91 │
│ trial_d8531    RUNNING            2   rmsprop         relu                                   64                 64                  3          0.00308065         106 │
│ trial_d8531    RUNNING            4   adam            relu                                  128                128                  5          0.000175915        124 │
│ trial_d8531    RUNNING            4   adam            relu                                  128                 32                  3          1.96033e-05        102 │
│ trial_d8531    RUNNING            4   rmsprop         tanh                                  128                128                  5          0.00244001         129 │
│ trial_d8531    RUNNING            3   adam            tanh                                   32                 64                  5          0.00455572          99 │
│ trial_d8531    RUNNING            3   adam            tanh                                   32                 64                  5          0.00361478         114 │
│ trial_d8531    RUNNING            4   rmsprop         relu                                   32                 32                  5          0.00235051         122 │
│ trial_d8531    RUNNING            3   rmsprop         relu                                   64                 32                  3          9.27852e-05         92 │
│ trial_d8531    RUNNING            4   adam            relu                                  128                 32                  3          8.42402e-05         67 │
│ trial_d8531    RUNNING            2   rmsprop         tanh                                   64                 32                  5          4.95152e-05         85 │
╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=1648238)[0m 
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[36m(train_cnn_ray_tune pid=1648240)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648221)[0m 
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[36m(train_cnn_ray_tune pid=1648204)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648203)[0m 
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[36m(train_cnn_ray_tune pid=1648203)[0m Epoch 7/85[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=1648246)[0m 
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[36m(train_cnn_ray_tune pid=1648239)[0m 
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[36m(train_cnn_ray_tune pid=1648246)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648240)[0m 
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[36m(train_cnn_ray_tune pid=1648221)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648227)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648252)[0m 
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[36m(train_cnn_ray_tune pid=1648252)[0m Epoch 7/67[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648202)[0m 
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[36m(train_cnn_ray_tune pid=1648202)[0m 
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[36m(train_cnn_ray_tune pid=1648204)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648246)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648249)[0m 
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[36m(train_cnn_ray_tune pid=1648203)[0m 
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[36m(train_cnn_ray_tune pid=1648249)[0m Epoch 4/129[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=1648204)[0m 
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[36m(train_cnn_ray_tune pid=1648203)[0m 
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[36m(train_cnn_ray_tune pid=1648203)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648249)[0m 
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[36m(train_cnn_ray_tune pid=1648202)[0m 
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[36m(train_cnn_ray_tune pid=1648202)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648252)[0m 
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[36m(train_cnn_ray_tune pid=1648239)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648227)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648250)[0m 
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[36m(train_cnn_ray_tune pid=1648250)[0m Epoch 8/102[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648240)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648227)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648251)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648203)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648194)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m Epoch 9/127[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648203)[0m 
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[36m(train_cnn_ray_tune pid=1648240)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648221)[0m 
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[36m(train_cnn_ray_tune pid=1648248)[0m 
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[36m(train_cnn_ray_tune pid=1648221)[0m 
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[36m(train_cnn_ray_tune pid=1648250)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m Epoch 6/93[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=1648248)[0m 
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[36m(train_cnn_ray_tune pid=1648227)[0m 
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[36m(train_cnn_ray_tune pid=1648252)[0m 
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[36m(train_cnn_ray_tune pid=1648240)[0m 
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[36m(train_cnn_ray_tune pid=1648250)[0m 
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Trial status: 20 RUNNING
Current time: 2025-10-28 12:34:25. Total running time: 1min 30s
Logical resource usage: 20.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status       N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs │
├───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_d8531    RUNNING            3   rmsprop         tanh                                   32                 64                  5          9.38039e-05         66 │
│ trial_d8531    RUNNING            4   rmsprop         tanh                                   32                128                  5          0.00287631          87 │
│ trial_d8531    RUNNING            3   adam            tanh                                  128                128                  5          9.43275e-05         83 │
│ trial_d8531    RUNNING            3   adam            tanh                                  128                 64                  5          0.00473849          83 │
│ trial_d8531    RUNNING            4   rmsprop         tanh                                  128                 32                  3          2.75956e-05        127 │
│ trial_d8531    RUNNING            2   adam            tanh                                   32                 32                  3          5.99053e-05         93 │
│ trial_d8531    RUNNING            3   adam            tanh                                   32                 64                  5          2.55695e-05         54 │
│ trial_d8531    RUNNING            3   adam            tanh                                   32                128                  5          0.00225539          99 │
│ trial_d8531    RUNNING            2   adam            tanh                                  128                128                  3          6.55104e-05         81 │
│ trial_d8531    RUNNING            3   rmsprop         relu                                   32                 64                  3          0.000151011         91 │
│ trial_d8531    RUNNING            2   rmsprop         relu                                   64                 64                  3          0.00308065         106 │
│ trial_d8531    RUNNING            4   adam            relu                                  128                128                  5          0.000175915        124 │
│ trial_d8531    RUNNING            4   adam            relu                                  128                 32                  3          1.96033e-05        102 │
│ trial_d8531    RUNNING            4   rmsprop         tanh                                  128                128                  5          0.00244001         129 │
│ trial_d8531    RUNNING            3   adam            tanh                                   32                 64                  5          0.00455572          99 │
│ trial_d8531    RUNNING            3   adam            tanh                                   32                 64                  5          0.00361478         114 │
│ trial_d8531    RUNNING            4   rmsprop         relu                                   32                 32                  5          0.00235051         122 │
│ trial_d8531    RUNNING            3   rmsprop         relu                                   64                 32                  3          9.27852e-05         92 │
│ trial_d8531    RUNNING            4   adam            relu                                  128                 32                  3          8.42402e-05         67 │
│ trial_d8531    RUNNING            2   rmsprop         tanh                                   64                 32                  5          4.95152e-05         85 │
╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648252)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648248)[0m 
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[36m(train_cnn_ray_tune pid=1648221)[0m 
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[36m(train_cnn_ray_tune pid=1648204)[0m 
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[36m(train_cnn_ray_tune pid=1648246)[0m 
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[36m(train_cnn_ray_tune pid=1648249)[0m 
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[36m(train_cnn_ray_tune pid=1648202)[0m 
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[36m(train_cnn_ray_tune pid=1648249)[0m Epoch 5/129[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=1648252)[0m 
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[36m(train_cnn_ray_tune pid=1648251)[0m 
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[36m(train_cnn_ray_tune pid=1648248)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m Epoch 6/83[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=1648250)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648204)[0m 
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[36m(train_cnn_ray_tune pid=1648203)[0m 
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[36m(train_cnn_ray_tune pid=1648248)[0m 
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[36m(train_cnn_ray_tune pid=1648221)[0m 
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[36m(train_cnn_ray_tune pid=1648227)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648202)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648251)[0m 
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[36m(train_cnn_ray_tune pid=1648251)[0m Epoch 4/99[32m [repeated 10x across cluster][0m
[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648246)[0m 
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[36m(train_cnn_ray_tune pid=1648248)[0m 
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[36m(train_cnn_ray_tune pid=1648203)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648202)[0m 
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[36m(train_cnn_ray_tune pid=1648202)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648203)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m Epoch 12/127[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=1648250)[0m 
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[36m(train_cnn_ray_tune pid=1648204)[0m 
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[36m(train_cnn_ray_tune pid=1648203)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648239)[0m 
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[36m(train_cnn_ray_tune pid=1648240)[0m 
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[36m(train_cnn_ray_tune pid=1648248)[0m 
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[36m(train_cnn_ray_tune pid=1648238)[0m 
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[36m(train_cnn_ray_tune pid=1648251)[0m 
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[36m(train_cnn_ray_tune pid=1648252)[0m 
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[36m(train_cnn_ray_tune pid=1648246)[0m 
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[36m(train_cnn_ray_tune pid=1648246)[0m Epoch 10/106[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=1648249)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648227)[0m 
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[36m(train_cnn_ray_tune pid=1648252)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648248)[0m 
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[36m(train_cnn_ray_tune pid=1648251)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648239)[0m 
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[36m(train_cnn_ray_tune pid=1648221)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m Epoch 8/93[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648194)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648204)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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Trial status: 20 RUNNING
Current time: 2025-10-28 12:34:55. Total running time: 2min 0s
Logical resource usage: 20.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status       N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs │
├───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_d8531    RUNNING            3   rmsprop         tanh                                   32                 64                  5          9.38039e-05         66 │
│ trial_d8531    RUNNING            4   rmsprop         tanh                                   32                128                  5          0.00287631          87 │
│ trial_d8531    RUNNING            3   adam            tanh                                  128                128                  5          9.43275e-05         83 │
│ trial_d8531    RUNNING            3   adam            tanh                                  128                 64                  5          0.00473849          83 │
│ trial_d8531    RUNNING            4   rmsprop         tanh                                  128                 32                  3          2.75956e-05        127 │
│ trial_d8531    RUNNING            2   adam            tanh                                   32                 32                  3          5.99053e-05         93 │
│ trial_d8531    RUNNING            3   adam            tanh                                   32                 64                  5          2.55695e-05         54 │
│ trial_d8531    RUNNING            3   adam            tanh                                   32                128                  5          0.00225539          99 │
│ trial_d8531    RUNNING            2   adam            tanh                                  128                128                  3          6.55104e-05         81 │
│ trial_d8531    RUNNING            3   rmsprop         relu                                   32                 64                  3          0.000151011         91 │
│ trial_d8531    RUNNING            2   rmsprop         relu                                   64                 64                  3          0.00308065         106 │
│ trial_d8531    RUNNING            4   adam            relu                                  128                128                  5          0.000175915        124 │
│ trial_d8531    RUNNING            4   adam            relu                                  128                 32                  3          1.96033e-05        102 │
│ trial_d8531    RUNNING            4   rmsprop         tanh                                  128                128                  5          0.00244001         129 │
│ trial_d8531    RUNNING            3   adam            tanh                                   32                 64                  5          0.00455572          99 │
│ trial_d8531    RUNNING            3   adam            tanh                                   32                 64                  5          0.00361478         114 │
│ trial_d8531    RUNNING            4   rmsprop         relu                                   32                 32                  5          0.00235051         122 │
│ trial_d8531    RUNNING            3   rmsprop         relu                                   64                 32                  3          9.27852e-05         92 │
│ trial_d8531    RUNNING            4   adam            relu                                  128                 32                  3          8.42402e-05         67 │
│ trial_d8531    RUNNING            2   rmsprop         tanh                                   64                 32                  5          4.95152e-05         85 │
╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=1648203)[0m 
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[36m(train_cnn_ray_tune pid=1648203)[0m 
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[36m(train_cnn_ray_tune pid=1648252)[0m 
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[36m(train_cnn_ray_tune pid=1648251)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648221)[0m 
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[36m(train_cnn_ray_tune pid=1648249)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648246)[0m 
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[36m(train_cnn_ray_tune pid=1648227)[0m 
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[36m(train_cnn_ray_tune pid=1648227)[0m Epoch 10/81[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=1648227)[0m 
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[36m(train_cnn_ray_tune pid=1648203)[0m 
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[36m(train_cnn_ray_tune pid=1648240)[0m 
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[36m(train_cnn_ray_tune pid=1648246)[0m 
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[36m(train_cnn_ray_tune pid=1648252)[0m 
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[36m(train_cnn_ray_tune pid=1648250)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648252)[0m 
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[36m(train_cnn_ray_tune pid=1648239)[0m 
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[36m(train_cnn_ray_tune pid=1648249)[0m 
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[36m(train_cnn_ray_tune pid=1648251)[0m 
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[36m(train_cnn_ray_tune pid=1648238)[0m 
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[36m(train_cnn_ray_tune pid=1648248)[0m 
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[36m(train_cnn_ray_tune pid=1648248)[0m Epoch 6/114[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648203)[0m 
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[36m(train_cnn_ray_tune pid=1648252)[0m 
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[36m(train_cnn_ray_tune pid=1648204)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648246)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648251)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648251)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648248)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648221)[0m 
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[36m(train_cnn_ray_tune pid=1648221)[0m Epoch 6/54[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648246)[0m 
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[36m(train_cnn_ray_tune pid=1648250)[0m 
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[36m(train_cnn_ray_tune pid=1648203)[0m 
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[36m(train_cnn_ray_tune pid=1648249)[0m 
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[36m(train_cnn_ray_tune pid=1648240)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648249)[0m 
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[36m(train_cnn_ray_tune pid=1648251)[0m 
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[36m(train_cnn_ray_tune pid=1648238)[0m 
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[36m(train_cnn_ray_tune pid=1648227)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648248)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m Epoch 6/91[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=1648249)[0m 
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[36m(train_cnn_ray_tune pid=1648204)[0m 
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[36m(train_cnn_ray_tune pid=1648251)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648204)[0m 
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[36m(train_cnn_ray_tune pid=1648249)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648221)[0m 
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[36m(train_cnn_ray_tune pid=1648194)[0m 
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[36m(train_cnn_ray_tune pid=1648194)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648246)[0m 
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[36m(train_cnn_ray_tune pid=1648246)[0m Epoch 13/106[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=1648239)[0m 
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[36m(train_cnn_ray_tune pid=1648240)[0m 
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[36m(train_cnn_ray_tune pid=1648249)[0m 
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[36m(train_cnn_ray_tune pid=1648240)[0m 
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[36m(train_cnn_ray_tune pid=1648250)[0m 
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[36m(train_cnn_ray_tune pid=1648248)[0m 
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[36m(train_cnn_ray_tune pid=1648251)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648250)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648202)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648227)[0m 
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[36m(train_cnn_ray_tune pid=1648202)[0m 
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[36m(train_cnn_ray_tune pid=1648227)[0m 
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[36m(train_cnn_ray_tune pid=1648252)[0m 
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[36m(train_cnn_ray_tune pid=1648252)[0m Epoch 16/67[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=1648252)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m 
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Trial status: 20 RUNNING
Current time: 2025-10-28 12:35:25. Total running time: 2min 30s
Logical resource usage: 20.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status       N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs │
├───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_d8531    RUNNING            3   rmsprop         tanh                                   32                 64                  5          9.38039e-05         66 │
│ trial_d8531    RUNNING            4   rmsprop         tanh                                   32                128                  5          0.00287631          87 │
│ trial_d8531    RUNNING            3   adam            tanh                                  128                128                  5          9.43275e-05         83 │
│ trial_d8531    RUNNING            3   adam            tanh                                  128                 64                  5          0.00473849          83 │
│ trial_d8531    RUNNING            4   rmsprop         tanh                                  128                 32                  3          2.75956e-05        127 │
│ trial_d8531    RUNNING            2   adam            tanh                                   32                 32                  3          5.99053e-05         93 │
│ trial_d8531    RUNNING            3   adam            tanh                                   32                 64                  5          2.55695e-05         54 │
│ trial_d8531    RUNNING            3   adam            tanh                                   32                128                  5          0.00225539          99 │
│ trial_d8531    RUNNING            2   adam            tanh                                  128                128                  3          6.55104e-05         81 │
│ trial_d8531    RUNNING            3   rmsprop         relu                                   32                 64                  3          0.000151011         91 │
│ trial_d8531    RUNNING            2   rmsprop         relu                                   64                 64                  3          0.00308065         106 │
│ trial_d8531    RUNNING            4   adam            relu                                  128                128                  5          0.000175915        124 │
│ trial_d8531    RUNNING            4   adam            relu                                  128                 32                  3          1.96033e-05        102 │
│ trial_d8531    RUNNING            4   rmsprop         tanh                                  128                128                  5          0.00244001         129 │
│ trial_d8531    RUNNING            3   adam            tanh                                   32                 64                  5          0.00455572          99 │
│ trial_d8531    RUNNING            3   adam            tanh                                   32                 64                  5          0.00361478         114 │
│ trial_d8531    RUNNING            4   rmsprop         relu                                   32                 32                  5          0.00235051         122 │
│ trial_d8531    RUNNING            3   rmsprop         relu                                   64                 32                  3          9.27852e-05         92 │
│ trial_d8531    RUNNING            4   adam            relu                                  128                 32                  3          8.42402e-05         67 │
│ trial_d8531    RUNNING            2   rmsprop         tanh                                   64                 32                  5          4.95152e-05         85 │
╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=1648246)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648238)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648202)[0m 
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[36m(train_cnn_ray_tune pid=1648238)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648250)[0m 
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[36m(train_cnn_ray_tune pid=1648238)[0m 
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[36m(train_cnn_ray_tune pid=1648203)[0m 
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[36m(train_cnn_ray_tune pid=1648203)[0m Epoch 17/85[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=1648240)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648249)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648251)[0m 
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[36m(train_cnn_ray_tune pid=1648249)[0m 
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[36m(train_cnn_ray_tune pid=1648251)[0m 
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[36m(train_cnn_ray_tune pid=1648239)[0m 
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[36m(train_cnn_ray_tune pid=1648252)[0m 
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[36m(train_cnn_ray_tune pid=1648202)[0m 
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[36m(train_cnn_ray_tune pid=1648251)[0m 
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[36m(train_cnn_ray_tune pid=1648249)[0m 
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[36m(train_cnn_ray_tune pid=1648221)[0m 
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[36m(train_cnn_ray_tune pid=1648221)[0m Epoch 7/54[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=1648221)[0m 
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[36m(train_cnn_ray_tune pid=1648227)[0m 
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[36m(train_cnn_ray_tune pid=1648203)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648202)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648239)[0m 
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[36m(train_cnn_ray_tune pid=1648221)[0m 
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[36m(train_cnn_ray_tune pid=1648203)[0m 
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[36m(train_cnn_ray_tune pid=1648203)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648203)[0m 
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[36m(train_cnn_ray_tune pid=1648203)[0m Epoch 18/85[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648250)[0m 
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[36m(train_cnn_ray_tune pid=1648194)[0m 
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[36m(train_cnn_ray_tune pid=1648204)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648249)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648240)[0m 
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[36m(train_cnn_ray_tune pid=1648250)[0m 
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[36m(train_cnn_ray_tune pid=1648250)[0m Epoch 18/102[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=1648250)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648240)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648250)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648194)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648203)[0m 
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[36m(train_cnn_ray_tune pid=1648202)[0m 
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[36m(train_cnn_ray_tune pid=1648202)[0m 
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[36m(train_cnn_ray_tune pid=1648202)[0m Epoch 5/87[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648203)[0m 
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[36m(train_cnn_ray_tune pid=1648252)[0m 
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[36m(train_cnn_ray_tune pid=1648194)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648238)[0m 
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[36m(train_cnn_ray_tune pid=1648248)[0m 
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[36m(train_cnn_ray_tune pid=1648249)[0m 
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[36m(train_cnn_ray_tune pid=1648240)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648239)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m Epoch 20/127[32m [repeated 11x across cluster][0m
[36m(train_cnn_ray_tune pid=1648251)[0m 
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Trial status: 20 RUNNING
Current time: 2025-10-28 12:35:55. Total running time: 3min 0s
Logical resource usage: 20.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status       N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs │
├───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_d8531    RUNNING            3   rmsprop         tanh                                   32                 64                  5          9.38039e-05         66 │
│ trial_d8531    RUNNING            4   rmsprop         tanh                                   32                128                  5          0.00287631          87 │
│ trial_d8531    RUNNING            3   adam            tanh                                  128                128                  5          9.43275e-05         83 │
│ trial_d8531    RUNNING            3   adam            tanh                                  128                 64                  5          0.00473849          83 │
│ trial_d8531    RUNNING            4   rmsprop         tanh                                  128                 32                  3          2.75956e-05        127 │
│ trial_d8531    RUNNING            2   adam            tanh                                   32                 32                  3          5.99053e-05         93 │
│ trial_d8531    RUNNING            3   adam            tanh                                   32                 64                  5          2.55695e-05         54 │
│ trial_d8531    RUNNING            3   adam            tanh                                   32                128                  5          0.00225539          99 │
│ trial_d8531    RUNNING            2   adam            tanh                                  128                128                  3          6.55104e-05         81 │
│ trial_d8531    RUNNING            3   rmsprop         relu                                   32                 64                  3          0.000151011         91 │
│ trial_d8531    RUNNING            2   rmsprop         relu                                   64                 64                  3          0.00308065         106 │
│ trial_d8531    RUNNING            4   adam            relu                                  128                128                  5          0.000175915        124 │
│ trial_d8531    RUNNING            4   adam            relu                                  128                 32                  3          1.96033e-05        102 │
│ trial_d8531    RUNNING            4   rmsprop         tanh                                  128                128                  5          0.00244001         129 │
│ trial_d8531    RUNNING            3   adam            tanh                                   32                 64                  5          0.00455572          99 │
│ trial_d8531    RUNNING            3   adam            tanh                                   32                 64                  5          0.00361478         114 │
│ trial_d8531    RUNNING            4   rmsprop         relu                                   32                 32                  5          0.00235051         122 │
│ trial_d8531    RUNNING            3   rmsprop         relu                                   64                 32                  3          9.27852e-05         92 │
│ trial_d8531    RUNNING            4   adam            relu                                  128                 32                  3          8.42402e-05         67 │
│ trial_d8531    RUNNING            2   rmsprop         tanh                                   64                 32                  5          4.95152e-05         85 │
╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=1648238)[0m 
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[36m(train_cnn_ray_tune pid=1648240)[0m 
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[36m(train_cnn_ray_tune pid=1648240)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648251)[0m 
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[36m(train_cnn_ray_tune pid=1648202)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648203)[0m 
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[36m(train_cnn_ray_tune pid=1648203)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648252)[0m 
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[36m(train_cnn_ray_tune pid=1648252)[0m Epoch 20/67[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648221)[0m 
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[36m(train_cnn_ray_tune pid=1648238)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648252)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648248)[0m 
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[36m(train_cnn_ray_tune pid=1648240)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648249)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648250)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648204)[0m 
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[36m(train_cnn_ray_tune pid=1648204)[0m Epoch 19/83[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648194)[0m 
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[36m(train_cnn_ray_tune pid=1648240)[0m 
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[36m(train_cnn_ray_tune pid=1648240)[0m 
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[36m(train_cnn_ray_tune pid=1648246)[0m 
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[36m(train_cnn_ray_tune pid=1648240)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648202)[0m 
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[36m(train_cnn_ray_tune pid=1648203)[0m 
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[36m(train_cnn_ray_tune pid=1648252)[0m 
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[36m(train_cnn_ray_tune pid=1648252)[0m 
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[36m(train_cnn_ray_tune pid=1648252)[0m 
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[36m(train_cnn_ray_tune pid=1648252)[0m Epoch 21/67[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648240)[0m 
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[36m(train_cnn_ray_tune pid=1648251)[0m 
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[36m(train_cnn_ray_tune pid=1648251)[0m 
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[36m(train_cnn_ray_tune pid=1648221)[0m 
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[36m(train_cnn_ray_tune pid=1648202)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648194)[0m 
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[36m(train_cnn_ray_tune pid=1648194)[0m 
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[36m(train_cnn_ray_tune pid=1648202)[0m 
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[36m(train_cnn_ray_tune pid=1648249)[0m 
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[36m(train_cnn_ray_tune pid=1648248)[0m 
[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:05[0m 113ms/step - accuracy: 0.1562 - loss: 2.6440
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[36m(train_cnn_ray_tune pid=1648238)[0m 
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[36m(train_cnn_ray_tune pid=1648250)[0m 
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[36m(train_cnn_ray_tune pid=1648240)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m Epoch 10/124[32m [repeated 10x across cluster][0m
[36m(train_cnn_ray_tune pid=1648204)[0m 
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[36m(train_cnn_ray_tune pid=1648246)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648240)[0m 
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[36m(train_cnn_ray_tune pid=1648204)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648248)[0m 
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[36m(train_cnn_ray_tune pid=1648238)[0m 
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[36m(train_cnn_ray_tune pid=1648238)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648252)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648252)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m Epoch 12/83[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=1648250)[0m 
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[36m(train_cnn_ray_tune pid=1648240)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648203)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648251)[0m 
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[36m(train_cnn_ray_tune pid=1648248)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648227)[0m 
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[36m(train_cnn_ray_tune pid=1648238)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648246)[0m 
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[36m(train_cnn_ray_tune pid=1648221)[0m 
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[36m(train_cnn_ray_tune pid=1648240)[0m 
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[36m(train_cnn_ray_tune pid=1648240)[0m 
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[36m(train_cnn_ray_tune pid=1648240)[0m Epoch 19/92[32m [repeated 8x across cluster][0m
Trial status: 20 RUNNING
Current time: 2025-10-28 12:36:25. Total running time: 3min 30s
Logical resource usage: 20.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status       N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs │
├───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_d8531    RUNNING            3   rmsprop         tanh                                   32                 64                  5          9.38039e-05         66 │
│ trial_d8531    RUNNING            4   rmsprop         tanh                                   32                128                  5          0.00287631          87 │
│ trial_d8531    RUNNING            3   adam            tanh                                  128                128                  5          9.43275e-05         83 │
│ trial_d8531    RUNNING            3   adam            tanh                                  128                 64                  5          0.00473849          83 │
│ trial_d8531    RUNNING            4   rmsprop         tanh                                  128                 32                  3          2.75956e-05        127 │
│ trial_d8531    RUNNING            2   adam            tanh                                   32                 32                  3          5.99053e-05         93 │
│ trial_d8531    RUNNING            3   adam            tanh                                   32                 64                  5          2.55695e-05         54 │
│ trial_d8531    RUNNING            3   adam            tanh                                   32                128                  5          0.00225539          99 │
│ trial_d8531    RUNNING            2   adam            tanh                                  128                128                  3          6.55104e-05         81 │
│ trial_d8531    RUNNING            3   rmsprop         relu                                   32                 64                  3          0.000151011         91 │
│ trial_d8531    RUNNING            2   rmsprop         relu                                   64                 64                  3          0.00308065         106 │
│ trial_d8531    RUNNING            4   adam            relu                                  128                128                  5          0.000175915        124 │
│ trial_d8531    RUNNING            4   adam            relu                                  128                 32                  3          1.96033e-05        102 │
│ trial_d8531    RUNNING            4   rmsprop         tanh                                  128                128                  5          0.00244001         129 │
│ trial_d8531    RUNNING            3   adam            tanh                                   32                 64                  5          0.00455572          99 │
│ trial_d8531    RUNNING            3   adam            tanh                                   32                 64                  5          0.00361478         114 │
│ trial_d8531    RUNNING            4   rmsprop         relu                                   32                 32                  5          0.00235051         122 │
│ trial_d8531    RUNNING            3   rmsprop         relu                                   64                 32                  3          9.27852e-05         92 │
│ trial_d8531    RUNNING            4   adam            relu                                  128                 32                  3          8.42402e-05         67 │
│ trial_d8531    RUNNING            2   rmsprop         tanh                                   64                 32                  5          4.95152e-05         85 │
╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
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[36m(train_cnn_ray_tune pid=1648240)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648194)[0m 
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[36m(train_cnn_ray_tune pid=1648239)[0m 
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[36m(train_cnn_ray_tune pid=1648203)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648252)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648194)[0m 
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[36m(train_cnn_ray_tune pid=1648202)[0m 
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[36m(train_cnn_ray_tune pid=1648250)[0m 
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[36m(train_cnn_ray_tune pid=1648250)[0m Epoch 23/102[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648252)[0m 
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[36m(train_cnn_ray_tune pid=1648204)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648194)[0m 
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[36m(train_cnn_ray_tune pid=1648227)[0m 
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[36m(train_cnn_ray_tune pid=1648249)[0m 
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[36m(train_cnn_ray_tune pid=1648246)[0m 
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[36m(train_cnn_ray_tune pid=1648204)[0m 
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[36m(train_cnn_ray_tune pid=1648204)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m Epoch 13/83[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648240)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648246)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648248)[0m 
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[36m(train_cnn_ray_tune pid=1648248)[0m 
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[36m(train_cnn_ray_tune pid=1648239)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648194)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648250)[0m 
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[36m(train_cnn_ray_tune pid=1648250)[0m Epoch 24/102[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=1648249)[0m 
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[36m(train_cnn_ray_tune pid=1648250)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648252)[0m 
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[36m(train_cnn_ray_tune pid=1648203)[0m 
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[36m(train_cnn_ray_tune pid=1648248)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648252)[0m 
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[36m(train_cnn_ray_tune pid=1648252)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648204)[0m 
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[36m(train_cnn_ray_tune pid=1648204)[0m 
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[36m(train_cnn_ray_tune pid=1648203)[0m 
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[36m(train_cnn_ray_tune pid=1648203)[0m Epoch 25/85[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=1648246)[0m 
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[36m(train_cnn_ray_tune pid=1648246)[0m 
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[36m(train_cnn_ray_tune pid=1648246)[0m 
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[36m(train_cnn_ray_tune pid=1648246)[0m 
[1m18/86[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 13ms/step
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648246)[0m 
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[36m(train_cnn_ray_tune pid=1648246)[0m 
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[36m(train_cnn_ray_tune pid=1648246)[0m 
[1m38/86[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 13ms/step
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[36m(train_cnn_ray_tune pid=1648246)[0m 
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[36m(train_cnn_ray_tune pid=1648246)[0m 
[1m57/86[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 13ms/step
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[36m(train_cnn_ray_tune pid=1648246)[0m 
[1m67/86[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 13ms/step
[1m72/86[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 13ms/step
[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648246)[0m 
[1m77/86[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 13ms/step
[36m(train_cnn_ray_tune pid=1648227)[0m 
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[36m(train_cnn_ray_tune pid=1648246)[0m 
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[36m(train_cnn_ray_tune pid=1648203)[0m 
[1m  1/289[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m32s[0m 112ms/step - accuracy: 0.1875 - loss: 2.4826
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[36m(train_cnn_ray_tune pid=1648246)[0m 
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 15ms/step
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 15ms/step
[36m(train_cnn_ray_tune pid=1648203)[0m 
[1m 59/289[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m6s[0m 27ms/step - accuracy: 0.1598 - loss: 2.5818
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[36m(train_cnn_ray_tune pid=1648194)[0m 
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[36m(train_cnn_ray_tune pid=1648252)[0m 
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[36m(train_cnn_ray_tune pid=1648246)[0m 
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[36m(train_cnn_ray_tune pid=1648246)[0m 
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[36m(train_cnn_ray_tune pid=1648246)[0m 
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[36m(train_cnn_ray_tune pid=1648246)[0m 
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[36m(train_cnn_ray_tune pid=1648246)[0m 
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[36m(train_cnn_ray_tune pid=1648246)[0m 
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[36m(train_cnn_ray_tune pid=1648246)[0m 
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[36m(train_cnn_ray_tune pid=1648246)[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=1648246)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=1648240)[0m 2025-10-28 12:32:58.438646: 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=1648240)[0m 2025-10-28 12:32:58.460861: 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=1648252)[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=1648252)[0m E0000 00:00:1761651178.419545 1649530 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=1648252)[0m E0000 00:00:1761651178.427900 1649530 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=1648240)[0m W0000 00:00:1761651178.518110 1649537 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=1648240)[0m 2025-10-28 12:32:58.524238: 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=1648240)[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=1648250)[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=1648250)[0m   warnings.warn([32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=1648251)[0m 2025-10-28 12:33:01.638330: 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=1648251)[0m 2025-10-28 12:33:01.638380: 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=1648251)[0m 2025-10-28 12:33:01.638400: 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=1648251)[0m 2025-10-28 12:33:01.638409: 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=1648251)[0m 2025-10-28 12:33:01.638415: 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=1648251)[0m 2025-10-28 12:33:01.638419: 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=1648251)[0m 2025-10-28 12:33:01.638783: 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=1648251)[0m 2025-10-28 12:33:01.638827: 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=1648251)[0m 2025-10-28 12:33:01.638833: 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=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648246)[0m 
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[36m(train_cnn_ray_tune pid=1648246)[0m 
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[36m(train_cnn_ray_tune pid=1648246)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648249)[0m 
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[36m(train_cnn_ray_tune pid=1648246)[0m 
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[36m(train_cnn_ray_tune pid=1648246)[0m 
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[36m(train_cnn_ray_tune pid=1648246)[0m 
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[36m(train_cnn_ray_tune pid=1648246)[0m 
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[36m(train_cnn_ray_tune pid=1648246)[0m 
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[36m(train_cnn_ray_tune pid=1648246)[0m 
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[36m(train_cnn_ray_tune pid=1648246)[0m 
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[36m(train_cnn_ray_tune pid=1648246)[0m 
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[36m(train_cnn_ray_tune pid=1648246)[0m 
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[36m(train_cnn_ray_tune pid=1648246)[0m 
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[36m(train_cnn_ray_tune pid=1648246)[0m 
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Trial trial_d8531 finished iteration 1 at 2025-10-28 12:36:48. Total running time: 3min 53s
╭──────────────────────────────────────╮
│ Trial trial_d8531 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s              231.02 │
│ time_total_s                  231.02 │
│ training_iteration                 1 │
│ val_accuracy                 0.37205 │
╰──────────────────────────────────────╯

Trial trial_d8531 completed after 1 iterations at 2025-10-28 12:36:48. Total running time: 3min 53s
[36m(train_cnn_ray_tune pid=1648250)[0m 
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[36m(train_cnn_ray_tune pid=1648240)[0m 
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[36m(train_cnn_ray_tune pid=1648250)[0m 
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[36m(train_cnn_ray_tune pid=1648250)[0m 
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[36m(train_cnn_ray_tune pid=1648250)[0m Epoch 25/102[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=1648194)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648240)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648239)[0m 
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[36m(train_cnn_ray_tune pid=1648221)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648251)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648249)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648252)[0m 
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[36m(train_cnn_ray_tune pid=1648203)[0m 
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[36m(train_cnn_ray_tune pid=1648249)[0m Epoch 12/129[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=1648195)[0m 
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Trial status: 19 RUNNING | 1 TERMINATED
Current time: 2025-10-28 12:36:55. Total running time: 4min 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_d8531    RUNNING              3   rmsprop         tanh                                   32                 64                  5          9.38039e-05         66                                              │
│ trial_d8531    RUNNING              4   rmsprop         tanh                                   32                128                  5          0.00287631          87                                              │
│ trial_d8531    RUNNING              3   adam            tanh                                  128                128                  5          9.43275e-05         83                                              │
│ trial_d8531    RUNNING              3   adam            tanh                                  128                 64                  5          0.00473849          83                                              │
│ trial_d8531    RUNNING              4   rmsprop         tanh                                  128                 32                  3          2.75956e-05        127                                              │
│ trial_d8531    RUNNING              2   adam            tanh                                   32                 32                  3          5.99053e-05         93                                              │
│ trial_d8531    RUNNING              3   adam            tanh                                   32                 64                  5          2.55695e-05         54                                              │
│ trial_d8531    RUNNING              3   adam            tanh                                   32                128                  5          0.00225539          99                                              │
│ trial_d8531    RUNNING              2   adam            tanh                                  128                128                  3          6.55104e-05         81                                              │
│ trial_d8531    RUNNING              3   rmsprop         relu                                   32                 64                  3          0.000151011         91                                              │
│ trial_d8531    RUNNING              4   adam            relu                                  128                128                  5          0.000175915        124                                              │
│ trial_d8531    RUNNING              4   adam            relu                                  128                 32                  3          1.96033e-05        102                                              │
│ trial_d8531    RUNNING              4   rmsprop         tanh                                  128                128                  5          0.00244001         129                                              │
│ trial_d8531    RUNNING              3   adam            tanh                                   32                 64                  5          0.00455572          99                                              │
│ trial_d8531    RUNNING              3   adam            tanh                                   32                 64                  5          0.00361478         114                                              │
│ trial_d8531    RUNNING              4   rmsprop         relu                                   32                 32                  5          0.00235051         122                                              │
│ trial_d8531    RUNNING              3   rmsprop         relu                                   64                 32                  3          9.27852e-05         92                                              │
│ trial_d8531    RUNNING              4   adam            relu                                  128                 32                  3          8.42402e-05         67                                              │
│ trial_d8531    RUNNING              2   rmsprop         tanh                                   64                 32                  5          4.95152e-05         85                                              │
│ trial_d8531    TERMINATED           2   rmsprop         relu                                   64                 64                  3          0.00308065         106        1             231.02         0.372047 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648251)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648203)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648202)[0m 
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[36m(train_cnn_ray_tune pid=1648221)[0m 
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[36m(train_cnn_ray_tune pid=1648221)[0m 
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[36m(train_cnn_ray_tune pid=1648249)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648250)[0m 
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[36m(train_cnn_ray_tune pid=1648238)[0m 
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[36m(train_cnn_ray_tune pid=1648238)[0m Epoch 11/122[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=1648250)[0m 
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[36m(train_cnn_ray_tune pid=1648239)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648240)[0m 
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[36m(train_cnn_ray_tune pid=1648203)[0m 
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[36m(train_cnn_ray_tune pid=1648248)[0m 
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[36m(train_cnn_ray_tune pid=1648221)[0m 
[1m292/578[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m9s[0m 35ms/step - accuracy: 0.1264 - loss: 2.7731 
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[36m(train_cnn_ray_tune pid=1648248)[0m 
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[36m(train_cnn_ray_tune pid=1648248)[0m 
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[36m(train_cnn_ray_tune pid=1648227)[0m 
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[1m 89/145[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m3s[0m 69ms/step - accuracy: 0.1773 - loss: 2.5511
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[36m(train_cnn_ray_tune pid=1648196)[0m 
[1m  2/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 54ms/step - accuracy: 0.1055 - loss: 2.8433  
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[36m(train_cnn_ray_tune pid=1648239)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648251)[0m 
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[36m(train_cnn_ray_tune pid=1648251)[0m Epoch 10/99[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=1648251)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648203)[0m 
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[36m(train_cnn_ray_tune pid=1648204)[0m 
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[36m(train_cnn_ray_tune pid=1648248)[0m 
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[36m(train_cnn_ray_tune pid=1648239)[0m 
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[36m(train_cnn_ray_tune pid=1648240)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648240)[0m 
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[36m(train_cnn_ray_tune pid=1648202)[0m 
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[36m(train_cnn_ray_tune pid=1648227)[0m 
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[36m(train_cnn_ray_tune pid=1648238)[0m 
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[36m(train_cnn_ray_tune pid=1648248)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648227)[0m 
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[36m(train_cnn_ray_tune pid=1648252)[0m 
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[36m(train_cnn_ray_tune pid=1648252)[0m Epoch 28/67[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648240)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648204)[0m 
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[36m(train_cnn_ray_tune pid=1648204)[0m 
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[36m(train_cnn_ray_tune pid=1648204)[0m 
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[36m(train_cnn_ray_tune pid=1648204)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648239)[0m 
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[36m(train_cnn_ray_tune pid=1648204)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648204)[0m 
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[36m(train_cnn_ray_tune pid=1648204)[0m 
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[36m(train_cnn_ray_tune pid=1648240)[0m 
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[36m(train_cnn_ray_tune pid=1648248)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648204)[0m 
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[36m(train_cnn_ray_tune pid=1648249)[0m 
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[36m(train_cnn_ray_tune pid=1648204)[0m 
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[36m(train_cnn_ray_tune pid=1648221)[0m 
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[36m(train_cnn_ray_tune pid=1648204)[0m 
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[36m(train_cnn_ray_tune pid=1648251)[0m 
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[36m(train_cnn_ray_tune pid=1648204)[0m 
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[36m(train_cnn_ray_tune pid=1648204)[0m 
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[36m(train_cnn_ray_tune pid=1648204)[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=1648204)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=1648204)[0m 
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[36m(train_cnn_ray_tune pid=1648204)[0m 
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[36m(train_cnn_ray_tune pid=1648204)[0m 
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[36m(train_cnn_ray_tune pid=1648221)[0m 
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[36m(train_cnn_ray_tune pid=1648249)[0m Epoch 13/129[32m [repeated 6x across cluster][0m
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[36m(train_cnn_ray_tune pid=1648204)[0m 
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[36m(train_cnn_ray_tune pid=1648204)[0m 
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[36m(train_cnn_ray_tune pid=1648204)[0m 
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[36m(train_cnn_ray_tune pid=1648204)[0m 
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[36m(train_cnn_ray_tune pid=1648204)[0m 
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[36m(train_cnn_ray_tune pid=1648204)[0m 
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[36m(train_cnn_ray_tune pid=1648204)[0m 
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[36m(train_cnn_ray_tune pid=1648204)[0m 
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[36m(train_cnn_ray_tune pid=1648204)[0m 
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[36m(train_cnn_ray_tune pid=1648204)[0m 
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[36m(train_cnn_ray_tune pid=1648204)[0m 
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Trial trial_d8531 finished iteration 1 at 2025-10-28 12:37:16. Total running time: 4min 21s
╭──────────────────────────────────────╮
│ Trial trial_d8531 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             258.659 │
│ time_total_s                 258.659 │
│ training_iteration                 1 │
│ val_accuracy                 0.33194 │
╰──────────────────────────────────────╯

Trial trial_d8531 completed after 1 iterations at 2025-10-28 12:37:16. Total running time: 4min 21s
[36m(train_cnn_ray_tune pid=1648249)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648221)[0m 
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[36m(train_cnn_ray_tune pid=1648239)[0m 
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[36m(train_cnn_ray_tune pid=1648252)[0m 
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[36m(train_cnn_ray_tune pid=1648203)[0m 
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[36m(train_cnn_ray_tune pid=1648252)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648202)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m Epoch 30/127[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648194)[0m 
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[36m(train_cnn_ray_tune pid=1648249)[0m 
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[36m(train_cnn_ray_tune pid=1648202)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648248)[0m 
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[36m(train_cnn_ray_tune pid=1648248)[0m 
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[36m(train_cnn_ray_tune pid=1648240)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648252)[0m 
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[36m(train_cnn_ray_tune pid=1648252)[0m Epoch 30/67[32m [repeated 6x across cluster][0m

Trial status: 18 RUNNING | 2 TERMINATED
Current time: 2025-10-28 12:37:25. Total running time: 4min 30s
Logical resource usage: 18.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_d8531    RUNNING              3   rmsprop         tanh                                   32                 64                  5          9.38039e-05         66                                              │
│ trial_d8531    RUNNING              4   rmsprop         tanh                                   32                128                  5          0.00287631          87                                              │
│ trial_d8531    RUNNING              3   adam            tanh                                  128                128                  5          9.43275e-05         83                                              │
│ trial_d8531    RUNNING              4   rmsprop         tanh                                  128                 32                  3          2.75956e-05        127                                              │
│ trial_d8531    RUNNING              2   adam            tanh                                   32                 32                  3          5.99053e-05         93                                              │
│ trial_d8531    RUNNING              3   adam            tanh                                   32                 64                  5          2.55695e-05         54                                              │
│ trial_d8531    RUNNING              3   adam            tanh                                   32                128                  5          0.00225539          99                                              │
│ trial_d8531    RUNNING              2   adam            tanh                                  128                128                  3          6.55104e-05         81                                              │
│ trial_d8531    RUNNING              3   rmsprop         relu                                   32                 64                  3          0.000151011         91                                              │
│ trial_d8531    RUNNING              4   adam            relu                                  128                128                  5          0.000175915        124                                              │
│ trial_d8531    RUNNING              4   adam            relu                                  128                 32                  3          1.96033e-05        102                                              │
│ trial_d8531    RUNNING              4   rmsprop         tanh                                  128                128                  5          0.00244001         129                                              │
│ trial_d8531    RUNNING              3   adam            tanh                                   32                 64                  5          0.00455572          99                                              │
│ trial_d8531    RUNNING              3   adam            tanh                                   32                 64                  5          0.00361478         114                                              │
│ trial_d8531    RUNNING              4   rmsprop         relu                                   32                 32                  5          0.00235051         122                                              │
│ trial_d8531    RUNNING              3   rmsprop         relu                                   64                 32                  3          9.27852e-05         92                                              │
│ trial_d8531    RUNNING              4   adam            relu                                  128                 32                  3          8.42402e-05         67                                              │
│ trial_d8531    RUNNING              2   rmsprop         tanh                                   64                 32                  5          4.95152e-05         85                                              │
│ trial_d8531    TERMINATED           3   adam            tanh                                  128                 64                  5          0.00473849          83        1            258.659         0.331944 │
│ trial_d8531    TERMINATED           2   rmsprop         relu                                   64                 64                  3          0.00308065         106        1            231.02          0.372047 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648203)[0m 
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[36m(train_cnn_ray_tune pid=1648194)[0m 
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[36m(train_cnn_ray_tune pid=1648251)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648248)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648251)[0m 
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[36m(train_cnn_ray_tune pid=1648240)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648227)[0m 
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[36m(train_cnn_ray_tune pid=1648227)[0m Epoch 23/81[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=1648238)[0m 
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[36m(train_cnn_ray_tune pid=1648203)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648249)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648252)[0m 
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[36m(train_cnn_ray_tune pid=1648251)[0m 
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[36m(train_cnn_ray_tune pid=1648239)[0m 
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[36m(train_cnn_ray_tune pid=1648250)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648240)[0m 
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[36m(train_cnn_ray_tune pid=1648249)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m27s[0m 193ms/step - accuracy: 0.2500 - loss: 2.3706[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=1648239)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 49ms/step - accuracy: 0.0993 - loss: 2.7917 - val_accuracy: 0.1703 - val_loss: 2.4414[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=1648196)[0m Epoch 32/127[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=1648249)[0m 
[1m 32/145[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m12s[0m 113ms/step - accuracy: 0.2708 - loss: 2.1933
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[36m(train_cnn_ray_tune pid=1648194)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m4s[0m 46ms/step - accuracy: 0.1021 - loss: 2.7976[32m [repeated 305x across cluster][0m
[36m(train_cnn_ray_tune pid=1648248)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m18s[0m 127ms/step - accuracy: 0.2136 - loss: 2.3955 - val_accuracy: 0.3286 - val_loss: 2.2052
[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648221)[0m 
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[36m(train_cnn_ray_tune pid=1648239)[0m 
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[36m(train_cnn_ray_tune pid=1648203)[0m 
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[36m(train_cnn_ray_tune pid=1648250)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648240)[0m 
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[36m(train_cnn_ray_tune pid=1648252)[0m 
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[36m(train_cnn_ray_tune pid=1648252)[0m Epoch 32/67[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=1648194)[0m 
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[36m(train_cnn_ray_tune pid=1648227)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648248)[0m 
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[36m(train_cnn_ray_tune pid=1648221)[0m 
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[36m(train_cnn_ray_tune pid=1648240)[0m 
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[36m(train_cnn_ray_tune pid=1648238)[0m 
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[36m(train_cnn_ray_tune pid=1648240)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648202)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m Epoch 13/91[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648203)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648240)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648249)[0m 
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[36m(train_cnn_ray_tune pid=1648240)[0m 
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[36m(train_cnn_ray_tune pid=1648251)[0m 
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[36m(train_cnn_ray_tune pid=1648249)[0m 
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[36m(train_cnn_ray_tune pid=1648227)[0m 
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[36m(train_cnn_ray_tune pid=1648249)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m Epoch 34/127[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=1648249)[0m 
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[36m(train_cnn_ray_tune pid=1648249)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648194)[0m 
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[36m(train_cnn_ray_tune pid=1648251)[0m 
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[36m(train_cnn_ray_tune pid=1648202)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648250)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648252)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648250)[0m 
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[36m(train_cnn_ray_tune pid=1648249)[0m 
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[36m(train_cnn_ray_tune pid=1648203)[0m 
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[36m(train_cnn_ray_tune pid=1648203)[0m Epoch 33/85[32m [repeated 5x across cluster][0m
Trial status: 18 RUNNING | 2 TERMINATED
Current time: 2025-10-28 12:37:55. Total running time: 5min 0s
Logical resource usage: 18.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_d8531    RUNNING              3   rmsprop         tanh                                   32                 64                  5          9.38039e-05         66                                              │
│ trial_d8531    RUNNING              4   rmsprop         tanh                                   32                128                  5          0.00287631          87                                              │
│ trial_d8531    RUNNING              3   adam            tanh                                  128                128                  5          9.43275e-05         83                                              │
│ trial_d8531    RUNNING              4   rmsprop         tanh                                  128                 32                  3          2.75956e-05        127                                              │
│ trial_d8531    RUNNING              2   adam            tanh                                   32                 32                  3          5.99053e-05         93                                              │
│ trial_d8531    RUNNING              3   adam            tanh                                   32                 64                  5          2.55695e-05         54                                              │
│ trial_d8531    RUNNING              3   adam            tanh                                   32                128                  5          0.00225539          99                                              │
│ trial_d8531    RUNNING              2   adam            tanh                                  128                128                  3          6.55104e-05         81                                              │
│ trial_d8531    RUNNING              3   rmsprop         relu                                   32                 64                  3          0.000151011         91                                              │
│ trial_d8531    RUNNING              4   adam            relu                                  128                128                  5          0.000175915        124                                              │
│ trial_d8531    RUNNING              4   adam            relu                                  128                 32                  3          1.96033e-05        102                                              │
│ trial_d8531    RUNNING              4   rmsprop         tanh                                  128                128                  5          0.00244001         129                                              │
│ trial_d8531    RUNNING              3   adam            tanh                                   32                 64                  5          0.00455572          99                                              │
│ trial_d8531    RUNNING              3   adam            tanh                                   32                 64                  5          0.00361478         114                                              │
│ trial_d8531    RUNNING              4   rmsprop         relu                                   32                 32                  5          0.00235051         122                                              │
│ trial_d8531    RUNNING              3   rmsprop         relu                                   64                 32                  3          9.27852e-05         92                                              │
│ trial_d8531    RUNNING              4   adam            relu                                  128                 32                  3          8.42402e-05         67                                              │
│ trial_d8531    RUNNING              2   rmsprop         tanh                                   64                 32                  5          4.95152e-05         85                                              │
│ trial_d8531    TERMINATED           3   adam            tanh                                  128                 64                  5          0.00473849          83        1            258.659         0.331944 │
│ trial_d8531    TERMINATED           2   rmsprop         relu                                   64                 64                  3          0.00308065         106        1            231.02          0.372047 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=1648202)[0m 
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[36m(train_cnn_ray_tune pid=1648251)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648221)[0m 
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[36m(train_cnn_ray_tune pid=1648202)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648240)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648227)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648239)[0m 
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[36m(train_cnn_ray_tune pid=1648239)[0m Epoch 10/99[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648240)[0m 
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[36m(train_cnn_ray_tune pid=1648202)[0m 
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[36m(train_cnn_ray_tune pid=1648221)[0m 
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[36m(train_cnn_ray_tune pid=1648240)[0m 
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[36m(train_cnn_ray_tune pid=1648238)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648250)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m Epoch 36/127[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=1648249)[0m 
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[36m(train_cnn_ray_tune pid=1648240)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648249)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648239)[0m 
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[36m(train_cnn_ray_tune pid=1648240)[0m 
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[36m(train_cnn_ray_tune pid=1648249)[0m 
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[36m(train_cnn_ray_tune pid=1648240)[0m 
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[36m(train_cnn_ray_tune pid=1648249)[0m 
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[36m(train_cnn_ray_tune pid=1648248)[0m 
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[36m(train_cnn_ray_tune pid=1648251)[0m 
[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m46s[0m 80ms/step - accuracy: 0.3125 - loss: 2.3746[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648249)[0m 
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[36m(train_cnn_ray_tune pid=1648249)[0m 
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[36m(train_cnn_ray_tune pid=1648252)[0m 
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 51ms/step - accuracy: 0.1462 - loss: 2.5863 - val_accuracy: 0.1892 - val_loss: 2.3922[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=1648252)[0m Epoch 36/67[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=1648252)[0m 
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[36m(train_cnn_ray_tune pid=1648249)[0m 
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[36m(train_cnn_ray_tune pid=1648203)[0m 
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[36m(train_cnn_ray_tune pid=1648249)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648251)[0m 
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[36m(train_cnn_ray_tune pid=1648203)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648194)[0m 
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[36m(train_cnn_ray_tune pid=1648194)[0m Epoch 14/66[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648240)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648251)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648221)[0m 
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[36m(train_cnn_ray_tune pid=1648249)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648202)[0m 
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[36m(train_cnn_ray_tune pid=1648202)[0m 
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[36m(train_cnn_ray_tune pid=1648202)[0m 
[1m 9/86[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 23ms/step
[36m(train_cnn_ray_tune pid=1648202)[0m 
[1m12/86[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 23ms/step
[36m(train_cnn_ray_tune pid=1648202)[0m 
[1m16/86[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step
[36m(train_cnn_ray_tune pid=1648202)[0m 
[1m19/86[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step
[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648202)[0m 
[1m22/86[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m1s[0m 20ms/step
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[36m(train_cnn_ray_tune pid=1648202)[0m 
[1m28/86[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m1s[0m 20ms/step
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[36m(train_cnn_ray_tune pid=1648202)[0m 
[1m35/86[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m1s[0m 20ms/step
[1m38/86[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 20ms/step
[36m(train_cnn_ray_tune pid=1648196)[0m 
[1m  3/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 40ms/step - accuracy: 0.1120 - loss: 2.7857  [32m [repeated 20x across cluster][0m
[36m(train_cnn_ray_tune pid=1648202)[0m 
[1m41/86[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 20ms/step
[1m45/86[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 19ms/step
[36m(train_cnn_ray_tune pid=1648202)[0m 
[1m48/86[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 19ms/step
[36m(train_cnn_ray_tune pid=1648227)[0m 
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m10s[0m 69ms/step - accuracy: 0.1996 - loss: 2.4837 - val_accuracy: 0.2668 - val_loss: 2.2674[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=1648227)[0m Epoch 28/81[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=1648202)[0m 
[1m51/86[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 19ms/step
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[36m(train_cnn_ray_tune pid=1648202)[0m 
[1m57/86[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 19ms/step
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[36m(train_cnn_ray_tune pid=1648202)[0m 
[1m63/86[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 20ms/step
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[36m(train_cnn_ray_tune pid=1648202)[0m 
[1m70/86[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 19ms/step
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[36m(train_cnn_ray_tune pid=1648202)[0m 
[1m77/86[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 19ms/step
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[36m(train_cnn_ray_tune pid=1648202)[0m 
[1m83/86[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 19ms/step
[36m(train_cnn_ray_tune pid=1648202)[0m 
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 23ms/step
[36m(train_cnn_ray_tune pid=1648202)[0m 
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 23ms/step
[36m(train_cnn_ray_tune pid=1648202)[0m 
[1m  1/158[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 63ms/step
[36m(train_cnn_ray_tune pid=1648202)[0m 
[1m  4/158[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 19ms/step
[36m(train_cnn_ray_tune pid=1648250)[0m 
[1m  3/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 48ms/step - accuracy: 0.0846 - loss: 2.8928  
[1m  5/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 47ms/step - accuracy: 0.0919 - loss: 2.8616
[36m(train_cnn_ray_tune pid=1648202)[0m 
[1m  7/158[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 19ms/step
[36m(train_cnn_ray_tune pid=1648202)[0m 
[1m 10/158[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 20ms/step
[36m(train_cnn_ray_tune pid=1648202)[0m 
[1m 13/158[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 19ms/step
[1m 16/158[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 20ms/step
[36m(train_cnn_ray_tune pid=1648202)[0m 
[1m 21/158[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 18ms/step
[1m 24/158[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m2s[0m 18ms/step
[36m(train_cnn_ray_tune pid=1648202)[0m 
[1m 27/158[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m2s[0m 19ms/step
[36m(train_cnn_ray_tune pid=1648202)[0m 
[1m 30/158[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m2s[0m 19ms/step
[36m(train_cnn_ray_tune pid=1648202)[0m 
[1m 33/158[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m2s[0m 19ms/step
[1m 35/158[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m2s[0m 20ms/step
[36m(train_cnn_ray_tune pid=1648240)[0m 
[1m207/289[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m2s[0m 27ms/step - accuracy: 0.1797 - loss: 2.4697
[1m209/289[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m2s[0m 28ms/step - accuracy: 0.1797 - loss: 2.4697[32m [repeated 252x across cluster][0m
[36m(train_cnn_ray_tune pid=1648196)[0m 
[1m 71/145[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m3s[0m 43ms/step - accuracy: 0.1061 - loss: 2.7598[32m [repeated 292x across cluster][0m
[36m(train_cnn_ray_tune pid=1648202)[0m 
[1m 39/158[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m2s[0m 19ms/step
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[36m(train_cnn_ray_tune pid=1648202)[0m 
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[36m(train_cnn_ray_tune pid=1648202)[0m 
[1m 52/158[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m2s[0m 19ms/step
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[36m(train_cnn_ray_tune pid=1648202)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648202)[0m 
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[36m(train_cnn_ray_tune pid=1648202)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648202)[0m 
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[36m(train_cnn_ray_tune pid=1648202)[0m 
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[36m(train_cnn_ray_tune pid=1648202)[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=1648202)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=1648202)[0m 
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[36m(train_cnn_ray_tune pid=1648202)[0m 
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[36m(train_cnn_ray_tune pid=1648202)[0m 
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[36m(train_cnn_ray_tune pid=1648202)[0m 
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[36m(train_cnn_ray_tune pid=1648202)[0m 
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[36m(train_cnn_ray_tune pid=1648202)[0m 
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[36m(train_cnn_ray_tune pid=1648249)[0m 
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[36m(train_cnn_ray_tune pid=1648202)[0m 
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[36m(train_cnn_ray_tune pid=1648202)[0m 
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[36m(train_cnn_ray_tune pid=1648240)[0m 
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[36m(train_cnn_ray_tune pid=1648202)[0m 
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[36m(train_cnn_ray_tune pid=1648202)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648202)[0m 
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[36m(train_cnn_ray_tune pid=1648202)[0m 
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[36m(train_cnn_ray_tune pid=1648202)[0m 
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[36m(train_cnn_ray_tune pid=1648250)[0m 
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Trial trial_d8531 finished iteration 1 at 2025-10-28 12:38:24. Total running time: 5min 30s
╭──────────────────────────────────────╮
│ Trial trial_d8531 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             327.103 │
│ time_total_s                 327.103 │
│ training_iteration                 1 │
│ val_accuracy                 0.30276 │
╰──────────────────────────────────────╯

Trial trial_d8531 completed after 1 iterations at 2025-10-28 12:38:24. Total running time: 5min 30s
[36m(train_cnn_ray_tune pid=1648250)[0m 
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[36m(train_cnn_ray_tune pid=1648249)[0m 
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[36m(train_cnn_ray_tune pid=1648249)[0m 
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[36m(train_cnn_ray_tune pid=1648249)[0m 
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[36m(train_cnn_ray_tune pid=1648252)[0m 
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[36m(train_cnn_ray_tune pid=1648252)[0m Epoch 38/67[32m [repeated 8x across cluster][0m

Trial status: 17 RUNNING | 3 TERMINATED
Current time: 2025-10-28 12:38:25. Total running time: 5min 30s
Logical resource usage: 17.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_d8531    RUNNING              3   rmsprop         tanh                                   32                 64                  5          9.38039e-05         66                                              │
│ trial_d8531    RUNNING              3   adam            tanh                                  128                128                  5          9.43275e-05         83                                              │
│ trial_d8531    RUNNING              4   rmsprop         tanh                                  128                 32                  3          2.75956e-05        127                                              │
│ trial_d8531    RUNNING              2   adam            tanh                                   32                 32                  3          5.99053e-05         93                                              │
│ trial_d8531    RUNNING              3   adam            tanh                                   32                 64                  5          2.55695e-05         54                                              │
│ trial_d8531    RUNNING              3   adam            tanh                                   32                128                  5          0.00225539          99                                              │
│ trial_d8531    RUNNING              2   adam            tanh                                  128                128                  3          6.55104e-05         81                                              │
│ trial_d8531    RUNNING              3   rmsprop         relu                                   32                 64                  3          0.000151011         91                                              │
│ trial_d8531    RUNNING              4   adam            relu                                  128                128                  5          0.000175915        124                                              │
│ trial_d8531    RUNNING              4   adam            relu                                  128                 32                  3          1.96033e-05        102                                              │
│ trial_d8531    RUNNING              4   rmsprop         tanh                                  128                128                  5          0.00244001         129                                              │
│ trial_d8531    RUNNING              3   adam            tanh                                   32                 64                  5          0.00455572          99                                              │
│ trial_d8531    RUNNING              3   adam            tanh                                   32                 64                  5          0.00361478         114                                              │
│ trial_d8531    RUNNING              4   rmsprop         relu                                   32                 32                  5          0.00235051         122                                              │
│ trial_d8531    RUNNING              3   rmsprop         relu                                   64                 32                  3          9.27852e-05         92                                              │
│ trial_d8531    RUNNING              4   adam            relu                                  128                 32                  3          8.42402e-05         67                                              │
│ trial_d8531    RUNNING              2   rmsprop         tanh                                   64                 32                  5          4.95152e-05         85                                              │
│ trial_d8531    TERMINATED           4   rmsprop         tanh                                   32                128                  5          0.00287631          87        1            327.103         0.30276  │
│ trial_d8531    TERMINATED           3   adam            tanh                                  128                 64                  5          0.00473849          83        1            258.659         0.331944 │
│ trial_d8531    TERMINATED           2   rmsprop         relu                                   64                 64                  3          0.00308065         106        1            231.02          0.372047 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=1648240)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648239)[0m 
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[36m(train_cnn_ray_tune pid=1648248)[0m 
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[36m(train_cnn_ray_tune pid=1648203)[0m 
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[36m(train_cnn_ray_tune pid=1648248)[0m 
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[36m(train_cnn_ray_tune pid=1648248)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m20s[0m 117ms/step - accuracy: 0.2328 - loss: 2.3330 - val_accuracy: 0.3450 - val_loss: 2.1663
[36m(train_cnn_ray_tune pid=1648203)[0m 
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[1m129/289[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.1740 - loss: 2.5269[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648227)[0m 
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m9s[0m 64ms/step - accuracy: 0.1929 - loss: 2.4784 - val_accuracy: 0.2692 - val_loss: 2.2641[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=1648227)[0m Epoch 29/81[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648251)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648252)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648251)[0m 
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[36m(train_cnn_ray_tune pid=1648251)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648240)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648240)[0m 
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[36m(train_cnn_ray_tune pid=1648240)[0m Epoch 32/92[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648203)[0m 
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[36m(train_cnn_ray_tune pid=1648221)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648227)[0m 
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[36m(train_cnn_ray_tune pid=1648240)[0m 
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[36m(train_cnn_ray_tune pid=1648249)[0m 
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[36m(train_cnn_ray_tune pid=1648221)[0m 
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[36m(train_cnn_ray_tune pid=1648249)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m Epoch 41/127[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=1648248)[0m 
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[36m(train_cnn_ray_tune pid=1648203)[0m 
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[36m(train_cnn_ray_tune pid=1648249)[0m 
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[36m(train_cnn_ray_tune pid=1648249)[0m 
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[36m(train_cnn_ray_tune pid=1648249)[0m 
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[36m(train_cnn_ray_tune pid=1648251)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648249)[0m 
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[36m(train_cnn_ray_tune pid=1648249)[0m 
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[36m(train_cnn_ray_tune pid=1648249)[0m 
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[36m(train_cnn_ray_tune pid=1648249)[0m 
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[36m(train_cnn_ray_tune pid=1648249)[0m 
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[36m(train_cnn_ray_tune pid=1648249)[0m 
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[36m(train_cnn_ray_tune pid=1648249)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648249)[0m 
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[36m(train_cnn_ray_tune pid=1648249)[0m 
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[36m(train_cnn_ray_tune pid=1648249)[0m 
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[36m(train_cnn_ray_tune pid=1648249)[0m 
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[36m(train_cnn_ray_tune pid=1648248)[0m 
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[36m(train_cnn_ray_tune pid=1648249)[0m 
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[36m(train_cnn_ray_tune pid=1648249)[0m 
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[36m(train_cnn_ray_tune pid=1648249)[0m 
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[36m(train_cnn_ray_tune pid=1648249)[0m 
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[36m(train_cnn_ray_tune pid=1648249)[0m 
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[36m(train_cnn_ray_tune pid=1648240)[0m 
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[36m(train_cnn_ray_tune pid=1648249)[0m 
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[36m(train_cnn_ray_tune pid=1648249)[0m 
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[36m(train_cnn_ray_tune pid=1648240)[0m 
[1m  1/289[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m25s[0m 87ms/step - accuracy: 0.1719 - loss: 2.4252[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648240)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m Epoch 18/124[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=1648248)[0m 
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[36m(train_cnn_ray_tune pid=1648252)[0m 
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[36m(train_cnn_ray_tune pid=1648248)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648238)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648227)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m Epoch 24/93[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=1648251)[0m 
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[36m(train_cnn_ray_tune pid=1648227)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648250)[0m 
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[36m(train_cnn_ray_tune pid=1648251)[0m 
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[36m(train_cnn_ray_tune pid=1648194)[0m 
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[36m(train_cnn_ray_tune pid=1648240)[0m 
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[36m(train_cnn_ray_tune pid=1648252)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648239)[0m 
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Trial status: 17 RUNNING | 3 TERMINATED
Current time: 2025-10-28 12:38:55. Total running time: 6min 0s
Logical resource usage: 17.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_d8531    RUNNING              3   rmsprop         tanh                                   32                 64                  5          9.38039e-05         66                                              │
│ trial_d8531    RUNNING              3   adam            tanh                                  128                128                  5          9.43275e-05         83                                              │
│ trial_d8531    RUNNING              4   rmsprop         tanh                                  128                 32                  3          2.75956e-05        127                                              │
│ trial_d8531    RUNNING              2   adam            tanh                                   32                 32                  3          5.99053e-05         93                                              │
│ trial_d8531    RUNNING              3   adam            tanh                                   32                 64                  5          2.55695e-05         54                                              │
│ trial_d8531    RUNNING              3   adam            tanh                                   32                128                  5          0.00225539          99                                              │
│ trial_d8531    RUNNING              2   adam            tanh                                  128                128                  3          6.55104e-05         81                                              │
│ trial_d8531    RUNNING              3   rmsprop         relu                                   32                 64                  3          0.000151011         91                                              │
│ trial_d8531    RUNNING              4   adam            relu                                  128                128                  5          0.000175915        124                                              │
│ trial_d8531    RUNNING              4   adam            relu                                  128                 32                  3          1.96033e-05        102                                              │
│ trial_d8531    RUNNING              4   rmsprop         tanh                                  128                128                  5          0.00244001         129                                              │
│ trial_d8531    RUNNING              3   adam            tanh                                   32                 64                  5          0.00455572          99                                              │
│ trial_d8531    RUNNING              3   adam            tanh                                   32                 64                  5          0.00361478         114                                              │
│ trial_d8531    RUNNING              4   rmsprop         relu                                   32                 32                  5          0.00235051         122                                              │
│ trial_d8531    RUNNING              3   rmsprop         relu                                   64                 32                  3          9.27852e-05         92                                              │
│ trial_d8531    RUNNING              4   adam            relu                                  128                 32                  3          8.42402e-05         67                                              │
│ trial_d8531    RUNNING              2   rmsprop         tanh                                   64                 32                  5          4.95152e-05         85                                              │
│ trial_d8531    TERMINATED           4   rmsprop         tanh                                   32                128                  5          0.00287631          87        1            327.103         0.30276  │
│ trial_d8531    TERMINATED           3   adam            tanh                                  128                 64                  5          0.00473849          83        1            258.659         0.331944 │
│ trial_d8531    TERMINATED           2   rmsprop         relu                                   64                 64                  3          0.00308065         106        1            231.02          0.372047 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=1648203)[0m 
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[36m(train_cnn_ray_tune pid=1648203)[0m 
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[36m(train_cnn_ray_tune pid=1648203)[0m Epoch 41/85[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=1648251)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648249)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648249)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648251)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648251)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m Epoch 44/127[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=1648248)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648248)[0m 
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[36m(train_cnn_ray_tune pid=1648221)[0m 
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[36m(train_cnn_ray_tune pid=1648240)[0m 
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[36m(train_cnn_ray_tune pid=1648203)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648252)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m Epoch 25/93[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=1648248)[0m 
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[36m(train_cnn_ray_tune pid=1648221)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648240)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648251)[0m 
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[36m(train_cnn_ray_tune pid=1648251)[0m 
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[36m(train_cnn_ray_tune pid=1648227)[0m 
[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m17s[0m 122ms/step - accuracy: 0.1797 - loss: 2.4416
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[36m(train_cnn_ray_tune pid=1648203)[0m 
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[36m(train_cnn_ray_tune pid=1648251)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648240)[0m 
[1m289/289[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m9s[0m 31ms/step - accuracy: 0.1910 - loss: 2.4260 - val_accuracy: 0.2664 - val_loss: 2.2694[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=1648240)[0m Epoch 36/92[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=1648194)[0m 
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[36m(train_cnn_ray_tune pid=1648249)[0m 
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[36m(train_cnn_ray_tune pid=1648227)[0m 
[1m131/145[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 54ms/step - accuracy: 0.2030 - loss: 2.4397
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[36m(train_cnn_ray_tune pid=1648239)[0m 
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[36m(train_cnn_ray_tune pid=1648249)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m2s[0m 100ms/step - accuracy: 0.2523 - loss: 2.2648[32m [repeated 61x across cluster][0m
[36m(train_cnn_ray_tune pid=1648194)[0m 
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[36m(train_cnn_ray_tune pid=1648252)[0m 
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[36m(train_cnn_ray_tune pid=1648227)[0m 
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[36m(train_cnn_ray_tune pid=1648252)[0m 
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[36m(train_cnn_ray_tune pid=1648227)[0m 
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[36m(train_cnn_ray_tune pid=1648227)[0m Epoch 34/81[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648203)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648221)[0m 
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[36m(train_cnn_ray_tune pid=1648248)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648248)[0m 
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[36m(train_cnn_ray_tune pid=1648249)[0m 
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[36m(train_cnn_ray_tune pid=1648248)[0m 
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[36m(train_cnn_ray_tune pid=1648240)[0m 
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[36m(train_cnn_ray_tune pid=1648248)[0m 
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[36m(train_cnn_ray_tune pid=1648248)[0m 
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[36m(train_cnn_ray_tune pid=1648248)[0m 
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[36m(train_cnn_ray_tune pid=1648251)[0m 
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[36m(train_cnn_ray_tune pid=1648252)[0m 
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[36m(train_cnn_ray_tune pid=1648252)[0m Epoch 46/67[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=1648248)[0m 
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[36m(train_cnn_ray_tune pid=1648248)[0m 
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[36m(train_cnn_ray_tune pid=1648248)[0m 
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[36m(train_cnn_ray_tune pid=1648248)[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=1648248)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=1648248)[0m 
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[36m(train_cnn_ray_tune pid=1648248)[0m 
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[36m(train_cnn_ray_tune pid=1648248)[0m 
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[36m(train_cnn_ray_tune pid=1648248)[0m 
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[36m(train_cnn_ray_tune pid=1648248)[0m 
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[36m(train_cnn_ray_tune pid=1648248)[0m 
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[36m(train_cnn_ray_tune pid=1648248)[0m 
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[36m(train_cnn_ray_tune pid=1648248)[0m 
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Trial trial_d8531 finished iteration 1 at 2025-10-28 12:39:22. Total running time: 6min 28s
╭──────────────────────────────────────╮
│ Trial trial_d8531 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             385.263 │
│ time_total_s                 385.263 │
│ training_iteration                 1 │
│ val_accuracy                 0.30653 │
╰──────────────────────────────────────╯

Trial trial_d8531 completed after 1 iterations at 2025-10-28 12:39:22. Total running time: 6min 28s
[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648194)[0m 
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[36m(train_cnn_ray_tune pid=1648250)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648227)[0m 
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[36m(train_cnn_ray_tune pid=1648238)[0m 
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[36m(train_cnn_ray_tune pid=1648239)[0m 
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[36m(train_cnn_ray_tune pid=1648203)[0m 
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Trial status: 16 RUNNING | 4 TERMINATED
Current time: 2025-10-28 12:39:25. Total running time: 6min 31s
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_d8531    RUNNING              3   rmsprop         tanh                                   32                 64                  5          9.38039e-05         66                                              │
│ trial_d8531    RUNNING              3   adam            tanh                                  128                128                  5          9.43275e-05         83                                              │
│ trial_d8531    RUNNING              4   rmsprop         tanh                                  128                 32                  3          2.75956e-05        127                                              │
│ trial_d8531    RUNNING              2   adam            tanh                                   32                 32                  3          5.99053e-05         93                                              │
│ trial_d8531    RUNNING              3   adam            tanh                                   32                 64                  5          2.55695e-05         54                                              │
│ trial_d8531    RUNNING              3   adam            tanh                                   32                128                  5          0.00225539          99                                              │
│ trial_d8531    RUNNING              2   adam            tanh                                  128                128                  3          6.55104e-05         81                                              │
│ trial_d8531    RUNNING              3   rmsprop         relu                                   32                 64                  3          0.000151011         91                                              │
│ trial_d8531    RUNNING              4   adam            relu                                  128                128                  5          0.000175915        124                                              │
│ trial_d8531    RUNNING              4   adam            relu                                  128                 32                  3          1.96033e-05        102                                              │
│ trial_d8531    RUNNING              4   rmsprop         tanh                                  128                128                  5          0.00244001         129                                              │
│ trial_d8531    RUNNING              3   adam            tanh                                   32                 64                  5          0.00455572          99                                              │
│ trial_d8531    RUNNING              4   rmsprop         relu                                   32                 32                  5          0.00235051         122                                              │
│ trial_d8531    RUNNING              3   rmsprop         relu                                   64                 32                  3          9.27852e-05         92                                              │
│ trial_d8531    RUNNING              4   adam            relu                                  128                 32                  3          8.42402e-05         67                                              │
│ trial_d8531    RUNNING              2   rmsprop         tanh                                   64                 32                  5          4.95152e-05         85                                              │
│ trial_d8531    TERMINATED           4   rmsprop         tanh                                   32                128                  5          0.00287631          87        1            327.103         0.30276  │
│ trial_d8531    TERMINATED           3   adam            tanh                                  128                 64                  5          0.00473849          83        1            258.659         0.331944 │
│ trial_d8531    TERMINATED           2   rmsprop         relu                                   64                 64                  3          0.00308065         106        1            231.02          0.372047 │
│ trial_d8531    TERMINATED           3   adam            tanh                                   32                 64                  5          0.00361478         114        1            385.263         0.306532 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=1648240)[0m 
[1m152/289[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m3s[0m 25ms/step - accuracy: 0.1940 - loss: 2.4425
[1m154/289[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m3s[0m 25ms/step - accuracy: 0.1940 - loss: 2.4424
[1m156/289[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m3s[0m 26ms/step - accuracy: 0.1940 - loss: 2.4423[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=1648194)[0m 
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[36m(train_cnn_ray_tune pid=1648194)[0m Epoch 18/66[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=1648252)[0m 
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[36m(train_cnn_ray_tune pid=1648251)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648249)[0m 
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[36m(train_cnn_ray_tune pid=1648251)[0m 
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[36m(train_cnn_ray_tune pid=1648240)[0m 
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[36m(train_cnn_ray_tune pid=1648251)[0m 
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[36m(train_cnn_ray_tune pid=1648249)[0m 
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[36m(train_cnn_ray_tune pid=1648249)[0m 
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[36m(train_cnn_ray_tune pid=1648250)[0m 
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[36m(train_cnn_ray_tune pid=1648238)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m Epoch 27/93[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=1648251)[0m 
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[36m(train_cnn_ray_tune pid=1648227)[0m 
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[36m(train_cnn_ray_tune pid=1648251)[0m 
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[36m(train_cnn_ray_tune pid=1648251)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648252)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648240)[0m 
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[36m(train_cnn_ray_tune pid=1648240)[0m 
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[36m(train_cnn_ray_tune pid=1648240)[0m Epoch 39/92[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=1648250)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648251)[0m 
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[36m(train_cnn_ray_tune pid=1648238)[0m 
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[36m(train_cnn_ray_tune pid=1648238)[0m 
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[36m(train_cnn_ray_tune pid=1648221)[0m 
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[36m(train_cnn_ray_tune pid=1648227)[0m 
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[36m(train_cnn_ray_tune pid=1648238)[0m 
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[36m(train_cnn_ray_tune pid=1648249)[0m 
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[36m(train_cnn_ray_tune pid=1648251)[0m 
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[36m(train_cnn_ray_tune pid=1648249)[0m 
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[36m(train_cnn_ray_tune pid=1648249)[0m Epoch 22/129[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=1648249)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648252)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648240)[0m 
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[36m(train_cnn_ray_tune pid=1648194)[0m 
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[36m(train_cnn_ray_tune pid=1648194)[0m 
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[36m(train_cnn_ray_tune pid=1648194)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m Epoch 51/127[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=1648203)[0m 
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[36m(train_cnn_ray_tune pid=1648250)[0m 
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[36m(train_cnn_ray_tune pid=1648250)[0m 
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[36m(train_cnn_ray_tune pid=1648238)[0m 
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[36m(train_cnn_ray_tune pid=1648251)[0m 
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[36m(train_cnn_ray_tune pid=1648227)[0m 
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[36m(train_cnn_ray_tune pid=1648251)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648239)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648203)[0m 
[1m289/289[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 23ms/step - accuracy: 0.1741 - loss: 2.4885 - val_accuracy: 0.2599 - val_loss: 2.3121[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=1648203)[0m Epoch 49/85[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648194)[0m 
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[36m(train_cnn_ray_tune pid=1648250)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648240)[0m 
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[36m(train_cnn_ray_tune pid=1648221)[0m 
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[36m(train_cnn_ray_tune pid=1648221)[0m 
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Trial status: 16 RUNNING | 4 TERMINATED
Current time: 2025-10-28 12:39:55. Total running time: 7min 1s
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_d8531    RUNNING              3   rmsprop         tanh                                   32                 64                  5          9.38039e-05         66                                              │
│ trial_d8531    RUNNING              3   adam            tanh                                  128                128                  5          9.43275e-05         83                                              │
│ trial_d8531    RUNNING              4   rmsprop         tanh                                  128                 32                  3          2.75956e-05        127                                              │
│ trial_d8531    RUNNING              2   adam            tanh                                   32                 32                  3          5.99053e-05         93                                              │
│ trial_d8531    RUNNING              3   adam            tanh                                   32                 64                  5          2.55695e-05         54                                              │
│ trial_d8531    RUNNING              3   adam            tanh                                   32                128                  5          0.00225539          99                                              │
│ trial_d8531    RUNNING              2   adam            tanh                                  128                128                  3          6.55104e-05         81                                              │
│ trial_d8531    RUNNING              3   rmsprop         relu                                   32                 64                  3          0.000151011         91                                              │
│ trial_d8531    RUNNING              4   adam            relu                                  128                128                  5          0.000175915        124                                              │
│ trial_d8531    RUNNING              4   adam            relu                                  128                 32                  3          1.96033e-05        102                                              │
│ trial_d8531    RUNNING              4   rmsprop         tanh                                  128                128                  5          0.00244001         129                                              │
│ trial_d8531    RUNNING              3   adam            tanh                                   32                 64                  5          0.00455572          99                                              │
│ trial_d8531    RUNNING              4   rmsprop         relu                                   32                 32                  5          0.00235051         122                                              │
│ trial_d8531    RUNNING              3   rmsprop         relu                                   64                 32                  3          9.27852e-05         92                                              │
│ trial_d8531    RUNNING              4   adam            relu                                  128                 32                  3          8.42402e-05         67                                              │
│ trial_d8531    RUNNING              2   rmsprop         tanh                                   64                 32                  5          4.95152e-05         85                                              │
│ trial_d8531    TERMINATED           4   rmsprop         tanh                                   32                128                  5          0.00287631          87        1            327.103         0.30276  │
│ trial_d8531    TERMINATED           3   adam            tanh                                  128                 64                  5          0.00473849          83        1            258.659         0.331944 │
│ trial_d8531    TERMINATED           2   rmsprop         relu                                   64                 64                  3          0.00308065         106        1            231.02          0.372047 │
│ trial_d8531    TERMINATED           3   adam            tanh                                   32                 64                  5          0.00361478         114        1            385.263         0.306532 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=1648227)[0m 
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[36m(train_cnn_ray_tune pid=1648227)[0m 
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[36m(train_cnn_ray_tune pid=1648227)[0m Epoch 39/81[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=1648227)[0m 
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[36m(train_cnn_ray_tune pid=1648249)[0m 
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[36m(train_cnn_ray_tune pid=1648251)[0m 
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[36m(train_cnn_ray_tune pid=1648249)[0m 
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[36m(train_cnn_ray_tune pid=1648251)[0m 
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[36m(train_cnn_ray_tune pid=1648240)[0m 
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[36m(train_cnn_ray_tune pid=1648194)[0m 
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[36m(train_cnn_ray_tune pid=1648238)[0m 
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[36m(train_cnn_ray_tune pid=1648238)[0m 
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[36m(train_cnn_ray_tune pid=1648238)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648250)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648251)[0m 
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[36m(train_cnn_ray_tune pid=1648251)[0m 
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[36m(train_cnn_ray_tune pid=1648251)[0m Epoch 19/99[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=1648240)[0m 
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[36m(train_cnn_ray_tune pid=1648203)[0m 
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[36m(train_cnn_ray_tune pid=1648194)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648251)[0m 
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[36m(train_cnn_ray_tune pid=1648227)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648239)[0m 
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[36m(train_cnn_ray_tune pid=1648239)[0m 
[1m 6/86[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 11ms/step  
[1m10/86[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 12ms/step
[36m(train_cnn_ray_tune pid=1648239)[0m 
[1m15/86[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 12ms/step
[1m19/86[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 13ms/step
[36m(train_cnn_ray_tune pid=1648250)[0m 
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[36m(train_cnn_ray_tune pid=1648239)[0m 
[1m24/86[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 13ms/step
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[36m(train_cnn_ray_tune pid=1648239)[0m 
[1m34/86[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 12ms/step
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[36m(train_cnn_ray_tune pid=1648239)[0m 
[1m46/86[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 12ms/step
[1m51/86[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 12ms/step
[36m(train_cnn_ray_tune pid=1648250)[0m 
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m6s[0m 43ms/step - accuracy: 0.1064 - loss: 2.7548 - val_accuracy: 0.2089 - val_loss: 2.4526[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=1648250)[0m Epoch 52/102[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=1648239)[0m 
[1m55/86[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 12ms/step
[1m61/86[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 12ms/step
[36m(train_cnn_ray_tune pid=1648239)[0m 
[1m64/86[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 12ms/step
[1m68/86[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 12ms/step
[36m(train_cnn_ray_tune pid=1648239)[0m 
[1m73/86[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 12ms/step
[1m77/86[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 12ms/step
[36m(train_cnn_ray_tune pid=1648239)[0m 
[1m82/86[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 12ms/step
[36m(train_cnn_ray_tune pid=1648239)[0m 
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 16ms/step
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 16ms/step
[36m(train_cnn_ray_tune pid=1648239)[0m 
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[36m(train_cnn_ray_tune pid=1648239)[0m 
[1m  6/158[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 13ms/step
[1m 10/158[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 13ms/step
[36m(train_cnn_ray_tune pid=1648239)[0m 
[1m 13/158[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 14ms/step
[1m 16/158[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 15ms/step
[36m(train_cnn_ray_tune pid=1648227)[0m 
[1m 82/145[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m2s[0m 45ms/step - accuracy: 0.2128 - loss: 2.3868
[1m 83/145[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m2s[0m 45ms/step - accuracy: 0.2128 - loss: 2.3868
[1m 84/145[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m2s[0m 45ms/step - accuracy: 0.2129 - loss: 2.3868[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1648239)[0m 
[1m 19/158[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step
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[36m(train_cnn_ray_tune pid=1648239)[0m 
[1m 26/158[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m2s[0m 17ms/step
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[36m(train_cnn_ray_tune pid=1648239)[0m 
[1m 34/158[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[36m(train_cnn_ray_tune pid=1648251)[0m 
[1m176/578[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m9s[0m 25ms/step - accuracy: 0.2375 - loss: 2.3081 
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[36m(train_cnn_ray_tune pid=1648239)[0m 
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[36m(train_cnn_ray_tune pid=1648249)[0m 
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[36m(train_cnn_ray_tune pid=1648239)[0m 
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[36m(train_cnn_ray_tune pid=1648239)[0m 
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[36m(train_cnn_ray_tune pid=1648239)[0m 
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[36m(train_cnn_ray_tune pid=1648249)[0m 
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[36m(train_cnn_ray_tune pid=1648239)[0m 
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[36m(train_cnn_ray_tune pid=1648239)[0m 
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[36m(train_cnn_ray_tune pid=1648239)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648239)[0m 
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[36m(train_cnn_ray_tune pid=1648239)[0m 
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[36m(train_cnn_ray_tune pid=1648239)[0m 
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[36m(train_cnn_ray_tune pid=1648239)[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=1648239)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=1648239)[0m 
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[36m(train_cnn_ray_tune pid=1648239)[0m 
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[36m(train_cnn_ray_tune pid=1648240)[0m 
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[36m(train_cnn_ray_tune pid=1648239)[0m 
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[36m(train_cnn_ray_tune pid=1648239)[0m 
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[36m(train_cnn_ray_tune pid=1648221)[0m 
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[36m(train_cnn_ray_tune pid=1648249)[0m 
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[36m(train_cnn_ray_tune pid=1648239)[0m 
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Trial trial_d8531 finished iteration 1 at 2025-10-28 12:40:09. Total running time: 7min 14s
╭──────────────────────────────────────╮
│ Trial trial_d8531 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             431.963 │
│ time_total_s                 431.963 │
│ training_iteration                 1 │
│ val_accuracy                  0.3385 │
╰──────────────────────────────────────╯

Trial trial_d8531 completed after 1 iterations at 2025-10-28 12:40:09. Total running time: 7min 14s
[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648227)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648238)[0m 
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[36m(train_cnn_ray_tune pid=1648238)[0m Epoch 21/122[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=1648251)[0m 
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[36m(train_cnn_ray_tune pid=1648221)[0m 
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[36m(train_cnn_ray_tune pid=1648227)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648250)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648238)[0m 
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[36m(train_cnn_ray_tune pid=1648238)[0m 
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[36m(train_cnn_ray_tune pid=1648240)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m Epoch 56/127[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=1648251)[0m 
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[36m(train_cnn_ray_tune pid=1648251)[0m 
[1m 8/86[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 9ms/step   
[1m14/86[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 9ms/step
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[36m(train_cnn_ray_tune pid=1648240)[0m 
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[36m(train_cnn_ray_tune pid=1648251)[0m 
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[36m(train_cnn_ray_tune pid=1648251)[0m 
[1m37/86[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 9ms/step
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[36m(train_cnn_ray_tune pid=1648251)[0m 
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[36m(train_cnn_ray_tune pid=1648251)[0m 
[1m54/86[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 9ms/step
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[36m(train_cnn_ray_tune pid=1648251)[0m 
[1m65/86[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 9ms/step
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[36m(train_cnn_ray_tune pid=1648251)[0m 
[1m77/86[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 9ms/step
[1m83/86[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 9ms/step
[36m(train_cnn_ray_tune pid=1648251)[0m 
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step
[36m(train_cnn_ray_tune pid=1648251)[0m 
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 13ms/step
[36m(train_cnn_ray_tune pid=1648251)[0m 
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[36m(train_cnn_ray_tune pid=1648251)[0m 
[1m  7/158[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 11ms/step
[36m(train_cnn_ray_tune pid=1648240)[0m 
[1m189/289[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m1s[0m 20ms/step - accuracy: 0.1968 - loss: 2.4075
[1m192/289[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m1s[0m 20ms/step - accuracy: 0.1968 - loss: 2.4073[32m [repeated 338x across cluster][0m
[36m(train_cnn_ray_tune pid=1648238)[0m 
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[36m(train_cnn_ray_tune pid=1648251)[0m 
[1m 12/158[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 10ms/step
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[36m(train_cnn_ray_tune pid=1648251)[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=1648251)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=1648251)[0m 
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[36m(train_cnn_ray_tune pid=1648251)[0m 
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[36m(train_cnn_ray_tune pid=1648251)[0m 
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[36m(train_cnn_ray_tune pid=1648194)[0m 
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[36m(train_cnn_ray_tune pid=1648251)[0m 
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[36m(train_cnn_ray_tune pid=1648251)[0m 
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[36m(train_cnn_ray_tune pid=1648251)[0m 
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[36m(train_cnn_ray_tune pid=1648251)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648251)[0m 
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[36m(train_cnn_ray_tune pid=1648251)[0m 
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[36m(train_cnn_ray_tune pid=1648251)[0m 
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[36m(train_cnn_ray_tune pid=1648249)[0m 
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Trial trial_d8531 finished iteration 1 at 2025-10-28 12:40:20. Total running time: 7min 25s
╭──────────────────────────────────────╮
│ Trial trial_d8531 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             442.457 │
│ time_total_s                 442.457 │
│ training_iteration                 1 │
│ val_accuracy                 0.30494 │
╰──────────────────────────────────────╯

Trial trial_d8531 completed after 1 iterations at 2025-10-28 12:40:20. Total running time: 7min 25s
[36m(train_cnn_ray_tune pid=1648227)[0m 
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[36m(train_cnn_ray_tune pid=1648252)[0m 
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[36m(train_cnn_ray_tune pid=1648240)[0m 
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[36m(train_cnn_ray_tune pid=1648240)[0m Epoch 45/92[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648227)[0m 
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[36m(train_cnn_ray_tune pid=1648203)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648221)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648221)[0m 
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Trial status: 14 RUNNING | 6 TERMINATED
Current time: 2025-10-28 12:40:25. Total running time: 7min 31s
Logical resource usage: 14.0/20 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_d8531    RUNNING              3   rmsprop         tanh                                   32                 64                  5          9.38039e-05         66                                              │
│ trial_d8531    RUNNING              3   adam            tanh                                  128                128                  5          9.43275e-05         83                                              │
│ trial_d8531    RUNNING              4   rmsprop         tanh                                  128                 32                  3          2.75956e-05        127                                              │
│ trial_d8531    RUNNING              2   adam            tanh                                   32                 32                  3          5.99053e-05         93                                              │
│ trial_d8531    RUNNING              3   adam            tanh                                   32                 64                  5          2.55695e-05         54                                              │
│ trial_d8531    RUNNING              2   adam            tanh                                  128                128                  3          6.55104e-05         81                                              │
│ trial_d8531    RUNNING              3   rmsprop         relu                                   32                 64                  3          0.000151011         91                                              │
│ trial_d8531    RUNNING              4   adam            relu                                  128                128                  5          0.000175915        124                                              │
│ trial_d8531    RUNNING              4   adam            relu                                  128                 32                  3          1.96033e-05        102                                              │
│ trial_d8531    RUNNING              4   rmsprop         tanh                                  128                128                  5          0.00244001         129                                              │
│ trial_d8531    RUNNING              4   rmsprop         relu                                   32                 32                  5          0.00235051         122                                              │
│ trial_d8531    RUNNING              3   rmsprop         relu                                   64                 32                  3          9.27852e-05         92                                              │
│ trial_d8531    RUNNING              4   adam            relu                                  128                 32                  3          8.42402e-05         67                                              │
│ trial_d8531    RUNNING              2   rmsprop         tanh                                   64                 32                  5          4.95152e-05         85                                              │
│ trial_d8531    TERMINATED           4   rmsprop         tanh                                   32                128                  5          0.00287631          87        1            327.103         0.30276  │
│ trial_d8531    TERMINATED           3   adam            tanh                                  128                 64                  5          0.00473849          83        1            258.659         0.331944 │
│ trial_d8531    TERMINATED           3   adam            tanh                                   32                128                  5          0.00225539          99        1            431.963         0.338495 │
│ trial_d8531    TERMINATED           2   rmsprop         relu                                   64                 64                  3          0.00308065         106        1            231.02          0.372047 │
│ trial_d8531    TERMINATED           3   adam            tanh                                   32                 64                  5          0.00455572          99        1            442.457         0.304943 │
│ trial_d8531    TERMINATED           3   adam            tanh                                   32                 64                  5          0.00361478         114        1            385.263         0.306532 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m5s[0m 34ms/step - accuracy: 0.1154 - loss: 2.6938 - val_accuracy: 0.1789 - val_loss: 2.3996[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=1648196)[0m Epoch 58/127[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=1648252)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648203)[0m 
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[36m(train_cnn_ray_tune pid=1648194)[0m 
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[36m(train_cnn_ray_tune pid=1648194)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648249)[0m 
[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m17s[0m 118ms/step - accuracy: 0.3281 - loss: 2.0664[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=1648249)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m13s[0m 23ms/step - accuracy: 0.2516 - loss: 2.2443 - val_accuracy: 0.3347 - val_loss: 2.1058[32m [repeated 10x across cluster][0m
[36m(train_cnn_ray_tune pid=1648247)[0m Epoch 23/91[32m [repeated 10x across cluster][0m
[36m(train_cnn_ray_tune pid=1648203)[0m 
[1m126/289[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m2s[0m 17ms/step - accuracy: 0.1790 - loss: 2.4692
[1m129/289[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m2s[0m 17ms/step - accuracy: 0.1791 - loss: 2.4692
[1m133/289[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m2s[0m 17ms/step - accuracy: 0.1792 - loss: 2.4693[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1648196)[0m 
[1m 89/145[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m1s[0m 31ms/step - accuracy: 0.1214 - loss: 2.6928
[1m 91/145[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m1s[0m 31ms/step - accuracy: 0.1214 - loss: 2.6925[32m [repeated 328x across cluster][0m
[36m(train_cnn_ray_tune pid=1648212)[0m 
[1m135/145[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 71ms/step - accuracy: 0.2768 - loss: 2.1684[32m [repeated 220x across cluster][0m
[36m(train_cnn_ray_tune pid=1648203)[0m 
[1m  4/289[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 19ms/step - accuracy: 0.1357 - loss: 2.5313 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[1m 96/578[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m9s[0m 21ms/step - accuracy: 0.2528 - loss: 2.2059 [32m [repeated 20x across cluster][0m
[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m43s[0m 76ms/step - accuracy: 0.1875 - loss: 2.5054
[1m  5/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 13ms/step - accuracy: 0.2041 - loss: 2.4640 [32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1648212)[0m 
[1m  2/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 67ms/step - accuracy: 0.2988 - loss: 2.0723  
[36m(train_cnn_ray_tune pid=1648252)[0m 
[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 87ms/step - accuracy: 0.1484 - loss: 2.4687[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=1648227)[0m 
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m6s[0m 43ms/step - accuracy: 0.2173 - loss: 2.3764 - val_accuracy: 0.2887 - val_loss: 2.2274[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=1648227)[0m Epoch 45/81[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=1648252)[0m 
[1m 31/145[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m3s[0m 30ms/step - accuracy: 0.1697 - loss: 2.4879
[1m 33/145[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m3s[0m 30ms/step - accuracy: 0.1701 - loss: 2.4873
[1m 35/145[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m3s[0m 30ms/step - accuracy: 0.1704 - loss: 2.4869[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=1648250)[0m 
[1m  3/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 33ms/step - accuracy: 0.0799 - loss: 2.7732  
[1m  5/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 34ms/step - accuracy: 0.0823 - loss: 2.7735
[36m(train_cnn_ray_tune pid=1648196)[0m 
[1m 87/145[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m1s[0m 31ms/step - accuracy: 0.1261 - loss: 2.6692
[1m 90/145[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m1s[0m 31ms/step - accuracy: 0.1260 - loss: 2.6694[32m [repeated 366x across cluster][0m
[36m(train_cnn_ray_tune pid=1648252)[0m 
[1m 88/145[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m1s[0m 30ms/step - accuracy: 0.1734 - loss: 2.4810[32m [repeated 197x across cluster][0m
[36m(train_cnn_ray_tune pid=1648252)[0m 
[1m  3/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 31ms/step - accuracy: 0.1632 - loss: 2.4791 
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[36m(train_cnn_ray_tune pid=1648221)[0m 
[1m101/578[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m10s[0m 21ms/step - accuracy: 0.1625 - loss: 2.6168
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[36m(train_cnn_ray_tune pid=1648221)[0m 
[1m 74/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 22ms/step - accuracy: 0.1639 - loss: 2.6115[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=1648221)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648238)[0m 
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[36m(train_cnn_ray_tune pid=1648240)[0m 
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[36m(train_cnn_ray_tune pid=1648240)[0m Epoch 48/92[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=1648249)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648227)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648194)[0m 
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[36m(train_cnn_ray_tune pid=1648194)[0m 
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[36m(train_cnn_ray_tune pid=1648203)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648252)[0m 
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m5s[0m 36ms/step - accuracy: 0.1750 - loss: 2.4722 - val_accuracy: 0.2184 - val_loss: 2.3540[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=1648252)[0m Epoch 61/67[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=1648196)[0m 
[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 79ms/step - accuracy: 0.1172 - loss: 2.6212
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648240)[0m 
[1m  4/289[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 17ms/step - accuracy: 0.1569 - loss: 2.5349 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
[1m 12/145[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 77ms/step - accuracy: 0.2838 - loss: 2.1535
[1m 13/145[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 75ms/step - accuracy: 0.2830 - loss: 2.1551 [32m [repeated 14x across cluster][0m
[36m(train_cnn_ray_tune pid=1648212)[0m 
[1m  7/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 79ms/step - accuracy: 0.2833 - loss: 2.1489[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648203)[0m 
[1m  1/289[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m20s[0m 73ms/step - accuracy: 0.1875 - loss: 2.4586[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=1648221)[0m 
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m14s[0m 25ms/step - accuracy: 0.1582 - loss: 2.6263 - val_accuracy: 0.2488 - val_loss: 2.2848[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=1648221)[0m Epoch 23/54[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648227)[0m 
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[36m(train_cnn_ray_tune pid=1648250)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648221)[0m 
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[36m(train_cnn_ray_tune pid=1648221)[0m 
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[36m(train_cnn_ray_tune pid=1648203)[0m 
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Trial status: 14 RUNNING | 6 TERMINATED
Current time: 2025-10-28 12:40:55. Total running time: 8min 1s
Logical resource usage: 14.0/20 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_d8531    RUNNING              3   rmsprop         tanh                                   32                 64                  5          9.38039e-05         66                                              │
│ trial_d8531    RUNNING              3   adam            tanh                                  128                128                  5          9.43275e-05         83                                              │
│ trial_d8531    RUNNING              4   rmsprop         tanh                                  128                 32                  3          2.75956e-05        127                                              │
│ trial_d8531    RUNNING              2   adam            tanh                                   32                 32                  3          5.99053e-05         93                                              │
│ trial_d8531    RUNNING              3   adam            tanh                                   32                 64                  5          2.55695e-05         54                                              │
│ trial_d8531    RUNNING              2   adam            tanh                                  128                128                  3          6.55104e-05         81                                              │
│ trial_d8531    RUNNING              3   rmsprop         relu                                   32                 64                  3          0.000151011         91                                              │
│ trial_d8531    RUNNING              4   adam            relu                                  128                128                  5          0.000175915        124                                              │
│ trial_d8531    RUNNING              4   adam            relu                                  128                 32                  3          1.96033e-05        102                                              │
│ trial_d8531    RUNNING              4   rmsprop         tanh                                  128                128                  5          0.00244001         129                                              │
│ trial_d8531    RUNNING              4   rmsprop         relu                                   32                 32                  5          0.00235051         122                                              │
│ trial_d8531    RUNNING              3   rmsprop         relu                                   64                 32                  3          9.27852e-05         92                                              │
│ trial_d8531    RUNNING              4   adam            relu                                  128                 32                  3          8.42402e-05         67                                              │
│ trial_d8531    RUNNING              2   rmsprop         tanh                                   64                 32                  5          4.95152e-05         85                                              │
│ trial_d8531    TERMINATED           4   rmsprop         tanh                                   32                128                  5          0.00287631          87        1            327.103         0.30276  │
│ trial_d8531    TERMINATED           3   adam            tanh                                  128                 64                  5          0.00473849          83        1            258.659         0.331944 │
│ trial_d8531    TERMINATED           3   adam            tanh                                   32                128                  5          0.00225539          99        1            431.963         0.338495 │
│ trial_d8531    TERMINATED           2   rmsprop         relu                                   64                 64                  3          0.00308065         106        1            231.02          0.372047 │
│ trial_d8531    TERMINATED           3   adam            tanh                                   32                 64                  5          0.00455572          99        1            442.457         0.304943 │
│ trial_d8531    TERMINATED           3   adam            tanh                                   32                 64                  5          0.00361478         114        1            385.263         0.306532 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=1648238)[0m 
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[36m(train_cnn_ray_tune pid=1648238)[0m 
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[36m(train_cnn_ray_tune pid=1648238)[0m 
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[36m(train_cnn_ray_tune pid=1648227)[0m 
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[36m(train_cnn_ray_tune pid=1648238)[0m 
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[36m(train_cnn_ray_tune pid=1648238)[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=1648238)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=1648203)[0m 
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[36m(train_cnn_ray_tune pid=1648203)[0m Epoch 60/85[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=1648238)[0m 
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[36m(train_cnn_ray_tune pid=1648238)[0m 
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[36m(train_cnn_ray_tune pid=1648238)[0m 
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[36m(train_cnn_ray_tune pid=1648249)[0m 
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[36m(train_cnn_ray_tune pid=1648238)[0m 
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[36m(train_cnn_ray_tune pid=1648238)[0m 
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[36m(train_cnn_ray_tune pid=1648238)[0m 
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Trial trial_d8531 finished iteration 1 at 2025-10-28 12:40:58. Total running time: 8min 3s
╭──────────────────────────────────────╮
│ Trial trial_d8531 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             480.512 │
│ time_total_s                 480.512 │
│ training_iteration                 1 │
│ val_accuracy                 0.31824 │
╰──────────────────────────────────────╯

Trial trial_d8531 completed after 1 iterations at 2025-10-28 12:40:58. Total running time: 8min 3s
[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648250)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648250)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648240)[0m 
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[36m(train_cnn_ray_tune pid=1648240)[0m Epoch 51/92[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=1648227)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648227)[0m 
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[36m(train_cnn_ray_tune pid=1648249)[0m 
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[36m(train_cnn_ray_tune pid=1648250)[0m 
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[36m(train_cnn_ray_tune pid=1648221)[0m 
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[36m(train_cnn_ray_tune pid=1648203)[0m 
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[36m(train_cnn_ray_tune pid=1648221)[0m 
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[36m(train_cnn_ray_tune pid=1648221)[0m 
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[36m(train_cnn_ray_tune pid=1648221)[0m 
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[36m(train_cnn_ray_tune pid=1648221)[0m 
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[36m(train_cnn_ray_tune pid=1648221)[0m 
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[36m(train_cnn_ray_tune pid=1648221)[0m 
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[36m(train_cnn_ray_tune pid=1648221)[0m 
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[36m(train_cnn_ray_tune pid=1648221)[0m 
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[36m(train_cnn_ray_tune pid=1648221)[0m 
[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m47s[0m 82ms/step - accuracy: 0.1875 - loss: 2.6644[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=1648221)[0m 
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[36m(train_cnn_ray_tune pid=1648221)[0m 
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[36m(train_cnn_ray_tune pid=1648221)[0m 
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[36m(train_cnn_ray_tune pid=1648221)[0m 
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m14s[0m 24ms/step - accuracy: 0.1565 - loss: 2.6171 - val_accuracy: 0.2569 - val_loss: 2.2847[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=1648221)[0m Epoch 24/54[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=1648221)[0m 
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[36m(train_cnn_ray_tune pid=1648221)[0m 
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[36m(train_cnn_ray_tune pid=1648221)[0m 
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[36m(train_cnn_ray_tune pid=1648221)[0m 
[1m 60/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 20ms/step - accuracy: 0.1419 - loss: 2.6726
[36m(train_cnn_ray_tune pid=1648221)[0m 
[1m 63/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 20ms/step - accuracy: 0.1424 - loss: 2.6711
[36m(train_cnn_ray_tune pid=1648221)[0m 
[1m 66/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 20ms/step - accuracy: 0.1427 - loss: 2.6698
[1m 69/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 20ms/step - accuracy: 0.1430 - loss: 2.6687
[36m(train_cnn_ray_tune pid=1648221)[0m 
[1m 72/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 20ms/step - accuracy: 0.1433 - loss: 2.6677
[1m 75/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 20ms/step - accuracy: 0.1436 - loss: 2.6667
[36m(train_cnn_ray_tune pid=1648252)[0m 
[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m14s[0m 101ms/step - accuracy: 0.1641 - loss: 2.5158
[1m  3/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 26ms/step - accuracy: 0.1706 - loss: 2.4973  [32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=1648212)[0m 
[1m  2/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 57ms/step - accuracy: 0.2930 - loss: 2.1377  
[1m  3/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 59ms/step - accuracy: 0.2891 - loss: 2.1363
[36m(train_cnn_ray_tune pid=1648196)[0m 
[1m 82/145[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m1s[0m 28ms/step - accuracy: 0.1252 - loss: 2.6649
[1m 84/145[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m1s[0m 28ms/step - accuracy: 0.1252 - loss: 2.6651[32m [repeated 322x across cluster][0m
[36m(train_cnn_ray_tune pid=1648240)[0m 
[1m 54/289[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m4s[0m 18ms/step - accuracy: 0.1967 - loss: 2.3694[32m [repeated 182x across cluster][0m
[36m(train_cnn_ray_tune pid=1648227)[0m 
[1m  3/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 34ms/step - accuracy: 0.1984 - loss: 2.3306 
[1m  5/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 33ms/step - accuracy: 0.1957 - loss: 2.3471[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=1648194)[0m 
[1m 78/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 20ms/step - accuracy: 0.2164 - loss: 2.3389
[1m 81/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 20ms/step - accuracy: 0.2167 - loss: 2.3391[32m [repeated 12x across cluster][0m
[36m(train_cnn_ray_tune pid=1648203)[0m 
[1m167/289[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m1s[0m 15ms/step - accuracy: 0.1837 - loss: 2.4630
[1m171/289[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m1s[0m 15ms/step - accuracy: 0.1837 - loss: 2.4628
[1m174/289[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m1s[0m 15ms/step - accuracy: 0.1837 - loss: 2.4626[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=1648194)[0m 
[1m 87/578[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m10s[0m 20ms/step - accuracy: 0.2171 - loss: 2.3395[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=1648247)[0m 
[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m46s[0m 80ms/step - accuracy: 0.0938 - loss: 2.4034[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=1648196)[0m 
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m5s[0m 33ms/step - accuracy: 0.1250 - loss: 2.6655 - val_accuracy: 0.1765 - val_loss: 2.3951[32m [repeated 11x across cluster][0m
[36m(train_cnn_ray_tune pid=1648196)[0m Epoch 67/127[32m [repeated 11x across cluster][0m
[36m(train_cnn_ray_tune pid=1648252)[0m 
[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m14s[0m 98ms/step - accuracy: 0.1797 - loss: 2.4635
[1m  3/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 29ms/step - accuracy: 0.1771 - loss: 2.4771 [32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=1648196)[0m 
[1m 85/145[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m1s[0m 29ms/step - accuracy: 0.1289 - loss: 2.6695
[1m 87/145[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m1s[0m 29ms/step - accuracy: 0.1288 - loss: 2.6693[32m [repeated 328x across cluster][0m
[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648227)[0m 
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[36m(train_cnn_ray_tune pid=1648249)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648250)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m Epoch 68/127[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=1648240)[0m 
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[36m(train_cnn_ray_tune pid=1648250)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648203)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648221)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648221)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648252)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m Epoch 69/127[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=1648252)[0m 
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[36m(train_cnn_ray_tune pid=1648252)[0m 
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[36m(train_cnn_ray_tune pid=1648252)[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=1648252)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=1648252)[0m 
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Trial trial_d8531 finished iteration 1 at 2025-10-28 12:41:25. Total running time: 8min 30s
╭──────────────────────────────────────╮
│ Trial trial_d8531 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             507.142 │
│ time_total_s                 507.142 │
│ training_iteration                 1 │
│ val_accuracy                  0.2291 │
╰──────────────────────────────────────╯

Trial trial_d8531 completed after 1 iterations at 2025-10-28 12:41:25. Total running time: 8min 30s

Trial status: 12 RUNNING | 8 TERMINATED
Current time: 2025-10-28 12:41:25. Total running time: 8min 31s
Logical resource usage: 12.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_d8531    RUNNING              3   rmsprop         tanh                                   32                 64                  5          9.38039e-05         66                                              │
│ trial_d8531    RUNNING              3   adam            tanh                                  128                128                  5          9.43275e-05         83                                              │
│ trial_d8531    RUNNING              4   rmsprop         tanh                                  128                 32                  3          2.75956e-05        127                                              │
│ trial_d8531    RUNNING              2   adam            tanh                                   32                 32                  3          5.99053e-05         93                                              │
│ trial_d8531    RUNNING              3   adam            tanh                                   32                 64                  5          2.55695e-05         54                                              │
│ trial_d8531    RUNNING              2   adam            tanh                                  128                128                  3          6.55104e-05         81                                              │
│ trial_d8531    RUNNING              3   rmsprop         relu                                   32                 64                  3          0.000151011         91                                              │
│ trial_d8531    RUNNING              4   adam            relu                                  128                128                  5          0.000175915        124                                              │
│ trial_d8531    RUNNING              4   adam            relu                                  128                 32                  3          1.96033e-05        102                                              │
│ trial_d8531    RUNNING              4   rmsprop         tanh                                  128                128                  5          0.00244001         129                                              │
│ trial_d8531    RUNNING              3   rmsprop         relu                                   64                 32                  3          9.27852e-05         92                                              │
│ trial_d8531    RUNNING              2   rmsprop         tanh                                   64                 32                  5          4.95152e-05         85                                              │
│ trial_d8531    TERMINATED           4   rmsprop         tanh                                   32                128                  5          0.00287631          87        1            327.103         0.30276  │
│ trial_d8531    TERMINATED           3   adam            tanh                                  128                 64                  5          0.00473849          83        1            258.659         0.331944 │
│ trial_d8531    TERMINATED           3   adam            tanh                                   32                128                  5          0.00225539          99        1            431.963         0.338495 │
│ trial_d8531    TERMINATED           2   rmsprop         relu                                   64                 64                  3          0.00308065         106        1            231.02          0.372047 │
│ trial_d8531    TERMINATED           3   adam            tanh                                   32                 64                  5          0.00455572          99        1            442.457         0.304943 │
│ trial_d8531    TERMINATED           3   adam            tanh                                   32                 64                  5          0.00361478         114        1            385.263         0.306532 │
│ trial_d8531    TERMINATED           4   rmsprop         relu                                   32                 32                  5          0.00235051         122        1            480.512         0.318245 │
│ trial_d8531    TERMINATED           4   adam            relu                                  128                 32                  3          8.42402e-05         67        1            507.142         0.229105 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648227)[0m 
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[36m(train_cnn_ray_tune pid=1648227)[0m 
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[36m(train_cnn_ray_tune pid=1648227)[0m Epoch 53/81[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=1648203)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648250)[0m 
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[36m(train_cnn_ray_tune pid=1648250)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648227)[0m 
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[36m(train_cnn_ray_tune pid=1648227)[0m Epoch 54/81[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=1648221)[0m 
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[36m(train_cnn_ray_tune pid=1648221)[0m 
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[36m(train_cnn_ray_tune pid=1648221)[0m 
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[36m(train_cnn_ray_tune pid=1648221)[0m 
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[36m(train_cnn_ray_tune pid=1648221)[0m 
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[36m(train_cnn_ray_tune pid=1648221)[0m 
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[36m(train_cnn_ray_tune pid=1648203)[0m 
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[36m(train_cnn_ray_tune pid=1648221)[0m 
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[36m(train_cnn_ray_tune pid=1648221)[0m 
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[36m(train_cnn_ray_tune pid=1648221)[0m 
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[36m(train_cnn_ray_tune pid=1648250)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648250)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648221)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648240)[0m 
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[36m(train_cnn_ray_tune pid=1648240)[0m Epoch 56/92[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=1648240)[0m 
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[36m(train_cnn_ray_tune pid=1648227)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648249)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648227)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m 
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m8s[0m 52ms/step - accuracy: 0.2393 - loss: 2.2893 - val_accuracy: 0.3300 - val_loss: 2.1677[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=1648195)[0m Epoch 39/83[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=1648250)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648240)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648221)[0m 
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[36m(train_cnn_ray_tune pid=1648221)[0m 
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[36m(train_cnn_ray_tune pid=1648249)[0m 
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[36m(train_cnn_ray_tune pid=1648249)[0m 
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[36m(train_cnn_ray_tune pid=1648249)[0m 
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[36m(train_cnn_ray_tune pid=1648249)[0m 
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[36m(train_cnn_ray_tune pid=1648249)[0m 
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[36m(train_cnn_ray_tune pid=1648249)[0m 
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[36m(train_cnn_ray_tune pid=1648249)[0m 
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[36m(train_cnn_ray_tune pid=1648240)[0m 
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[36m(train_cnn_ray_tune pid=1648249)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m Epoch 40/83[32m [repeated 10x across cluster][0m
[36m(train_cnn_ray_tune pid=1648194)[0m 
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[36m(train_cnn_ray_tune pid=1648249)[0m 
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[36m(train_cnn_ray_tune pid=1648249)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648249)[0m 
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[36m(train_cnn_ray_tune pid=1648249)[0m 
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[36m(train_cnn_ray_tune pid=1648249)[0m 
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[36m(train_cnn_ray_tune pid=1648249)[0m 
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[36m(train_cnn_ray_tune pid=1648249)[0m 
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[36m(train_cnn_ray_tune pid=1648249)[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=1648249)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=1648249)[0m 
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[36m(train_cnn_ray_tune pid=1648249)[0m 
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Trial trial_d8531 finished iteration 1 at 2025-10-28 12:41:48. Total running time: 8min 53s
╭──────────────────────────────────────╮
│ Trial trial_d8531 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             530.461 │
│ time_total_s                 530.461 │
│ training_iteration                 1 │
│ val_accuracy                  0.3377 │
╰──────────────────────────────────────╯

Trial trial_d8531 completed after 1 iterations at 2025-10-28 12:41:48. Total running time: 8min 53s
[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648240)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m Epoch 76/127[32m [repeated 9x across cluster][0m
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[36m(train_cnn_ray_tune pid=1648203)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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Trial status: 11 RUNNING | 9 TERMINATED
Current time: 2025-10-28 12:41:55. Total running time: 9min 1s
Logical resource usage: 11.0/20 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_d8531    RUNNING              3   rmsprop         tanh                                   32                 64                  5          9.38039e-05         66                                              │
│ trial_d8531    RUNNING              3   adam            tanh                                  128                128                  5          9.43275e-05         83                                              │
│ trial_d8531    RUNNING              4   rmsprop         tanh                                  128                 32                  3          2.75956e-05        127                                              │
│ trial_d8531    RUNNING              2   adam            tanh                                   32                 32                  3          5.99053e-05         93                                              │
│ trial_d8531    RUNNING              3   adam            tanh                                   32                 64                  5          2.55695e-05         54                                              │
│ trial_d8531    RUNNING              2   adam            tanh                                  128                128                  3          6.55104e-05         81                                              │
│ trial_d8531    RUNNING              3   rmsprop         relu                                   32                 64                  3          0.000151011         91                                              │
│ trial_d8531    RUNNING              4   adam            relu                                  128                128                  5          0.000175915        124                                              │
│ trial_d8531    RUNNING              4   adam            relu                                  128                 32                  3          1.96033e-05        102                                              │
│ trial_d8531    RUNNING              3   rmsprop         relu                                   64                 32                  3          9.27852e-05         92                                              │
│ trial_d8531    RUNNING              2   rmsprop         tanh                                   64                 32                  5          4.95152e-05         85                                              │
│ trial_d8531    TERMINATED           4   rmsprop         tanh                                   32                128                  5          0.00287631          87        1            327.103         0.30276  │
│ trial_d8531    TERMINATED           3   adam            tanh                                  128                 64                  5          0.00473849          83        1            258.659         0.331944 │
│ trial_d8531    TERMINATED           3   adam            tanh                                   32                128                  5          0.00225539          99        1            431.963         0.338495 │
│ trial_d8531    TERMINATED           2   rmsprop         relu                                   64                 64                  3          0.00308065         106        1            231.02          0.372047 │
│ trial_d8531    TERMINATED           4   rmsprop         tanh                                  128                128                  5          0.00244001         129        1            530.461         0.337701 │
│ trial_d8531    TERMINATED           3   adam            tanh                                   32                 64                  5          0.00455572          99        1            442.457         0.304943 │
│ trial_d8531    TERMINATED           3   adam            tanh                                   32                 64                  5          0.00361478         114        1            385.263         0.306532 │
│ trial_d8531    TERMINATED           4   rmsprop         relu                                   32                 32                  5          0.00235051         122        1            480.512         0.318245 │
│ trial_d8531    TERMINATED           4   adam            relu                                  128                 32                  3          8.42402e-05         67        1            507.142         0.229105 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648221)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m Epoch 30/91[32m [repeated 11x across cluster][0m
[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648221)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648203)[0m 
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[36m(train_cnn_ray_tune pid=1648203)[0m Epoch 73/85[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648203)[0m 
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[36m(train_cnn_ray_tune pid=1648250)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m Epoch 80/127[32m [repeated 14x across cluster][0m
[36m(train_cnn_ray_tune pid=1648250)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648227)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648203)[0m 
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[36m(train_cnn_ray_tune pid=1648203)[0m 
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[36m(train_cnn_ray_tune pid=1648227)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m Epoch 36/124[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=1648227)[0m 
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[36m(train_cnn_ray_tune pid=1648240)[0m 
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[36m(train_cnn_ray_tune pid=1648250)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648250)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648203)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m Epoch 45/93[32m [repeated 11x across cluster][0m
[36m(train_cnn_ray_tune pid=1648203)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648240)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648227)[0m 
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[36m(train_cnn_ray_tune pid=1648221)[0m 
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[36m(train_cnn_ray_tune pid=1648203)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m Epoch 84/127[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=1648203)[0m 
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[36m(train_cnn_ray_tune pid=1648250)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648240)[0m 
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Trial status: 11 RUNNING | 9 TERMINATED
Current time: 2025-10-28 12:42:25. Total running time: 9min 31s
Logical resource usage: 11.0/20 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_d8531    RUNNING              3   rmsprop         tanh                                   32                 64                  5          9.38039e-05         66                                              │
│ trial_d8531    RUNNING              3   adam            tanh                                  128                128                  5          9.43275e-05         83                                              │
│ trial_d8531    RUNNING              4   rmsprop         tanh                                  128                 32                  3          2.75956e-05        127                                              │
│ trial_d8531    RUNNING              2   adam            tanh                                   32                 32                  3          5.99053e-05         93                                              │
│ trial_d8531    RUNNING              3   adam            tanh                                   32                 64                  5          2.55695e-05         54                                              │
│ trial_d8531    RUNNING              2   adam            tanh                                  128                128                  3          6.55104e-05         81                                              │
│ trial_d8531    RUNNING              3   rmsprop         relu                                   32                 64                  3          0.000151011         91                                              │
│ trial_d8531    RUNNING              4   adam            relu                                  128                128                  5          0.000175915        124                                              │
│ trial_d8531    RUNNING              4   adam            relu                                  128                 32                  3          1.96033e-05        102                                              │
│ trial_d8531    RUNNING              3   rmsprop         relu                                   64                 32                  3          9.27852e-05         92                                              │
│ trial_d8531    RUNNING              2   rmsprop         tanh                                   64                 32                  5          4.95152e-05         85                                              │
│ trial_d8531    TERMINATED           4   rmsprop         tanh                                   32                128                  5          0.00287631          87        1            327.103         0.30276  │
│ trial_d8531    TERMINATED           3   adam            tanh                                  128                 64                  5          0.00473849          83        1            258.659         0.331944 │
│ trial_d8531    TERMINATED           3   adam            tanh                                   32                128                  5          0.00225539          99        1            431.963         0.338495 │
│ trial_d8531    TERMINATED           2   rmsprop         relu                                   64                 64                  3          0.00308065         106        1            231.02          0.372047 │
│ trial_d8531    TERMINATED           4   rmsprop         tanh                                  128                128                  5          0.00244001         129        1            530.461         0.337701 │
│ trial_d8531    TERMINATED           3   adam            tanh                                   32                 64                  5          0.00455572          99        1            442.457         0.304943 │
│ trial_d8531    TERMINATED           3   adam            tanh                                   32                 64                  5          0.00361478         114        1            385.263         0.306532 │
│ trial_d8531    TERMINATED           4   rmsprop         relu                                   32                 32                  5          0.00235051         122        1            480.512         0.318245 │
│ trial_d8531    TERMINATED           4   adam            relu                                  128                 32                  3          8.42402e-05         67        1            507.142         0.229105 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=1648227)[0m 
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[36m(train_cnn_ray_tune pid=1648203)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m Epoch 33/91[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648250)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m Epoch 47/93[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648194)[0m 
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[36m(train_cnn_ray_tune pid=1648240)[0m 
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[36m(train_cnn_ray_tune pid=1648250)[0m 
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[36m(train_cnn_ray_tune pid=1648227)[0m 
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[36m(train_cnn_ray_tune pid=1648250)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m Epoch 88/127[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=1648203)[0m 
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[36m(train_cnn_ray_tune pid=1648203)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648227)[0m 
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[36m(train_cnn_ray_tune pid=1648203)[0m 
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[36m(train_cnn_ray_tune pid=1648203)[0m 
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[36m(train_cnn_ray_tune pid=1648203)[0m Epoch 83/85[32m [repeated 11x across cluster][0m
[36m(train_cnn_ray_tune pid=1648203)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m Epoch 49/93[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=1648203)[0m 
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[36m(train_cnn_ray_tune pid=1648194)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648221)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m Epoch 50/83[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648203)[0m 
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[36m(train_cnn_ray_tune pid=1648203)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m39s[0m 69ms/step - accuracy: 0.1250 - loss: 2.6488
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[36m(train_cnn_ray_tune pid=1648240)[0m 
[1m  1/289[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m19s[0m 66ms/step - accuracy: 0.2188 - loss: 2.2063
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[36m(train_cnn_ray_tune pid=1648203)[0m 
[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m20s[0m 245ms/step
[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648203)[0m 
[1m 9/86[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 7ms/step   
[1m18/86[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 6ms/step
[36m(train_cnn_ray_tune pid=1648203)[0m 
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[36m(train_cnn_ray_tune pid=1648203)[0m 
[1m39/86[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 6ms/step
[1m49/86[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 6ms/step
[36m(train_cnn_ray_tune pid=1648203)[0m 
[1m59/86[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 6ms/step
[1m68/86[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 6ms/step
Trial status: 11 RUNNING | 9 TERMINATED
Current time: 2025-10-28 12:42:56. Total running time: 10min 1s
Logical resource usage: 11.0/20 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_d8531    RUNNING              3   rmsprop         tanh                                   32                 64                  5          9.38039e-05         66                                              │
│ trial_d8531    RUNNING              3   adam            tanh                                  128                128                  5          9.43275e-05         83                                              │
│ trial_d8531    RUNNING              4   rmsprop         tanh                                  128                 32                  3          2.75956e-05        127                                              │
│ trial_d8531    RUNNING              2   adam            tanh                                   32                 32                  3          5.99053e-05         93                                              │
│ trial_d8531    RUNNING              3   adam            tanh                                   32                 64                  5          2.55695e-05         54                                              │
│ trial_d8531    RUNNING              2   adam            tanh                                  128                128                  3          6.55104e-05         81                                              │
│ trial_d8531    RUNNING              3   rmsprop         relu                                   32                 64                  3          0.000151011         91                                              │
│ trial_d8531    RUNNING              4   adam            relu                                  128                128                  5          0.000175915        124                                              │
│ trial_d8531    RUNNING              4   adam            relu                                  128                 32                  3          1.96033e-05        102                                              │
│ trial_d8531    RUNNING              3   rmsprop         relu                                   64                 32                  3          9.27852e-05         92                                              │
│ trial_d8531    RUNNING              2   rmsprop         tanh                                   64                 32                  5          4.95152e-05         85                                              │
│ trial_d8531    TERMINATED           4   rmsprop         tanh                                   32                128                  5          0.00287631          87        1            327.103         0.30276  │
│ trial_d8531    TERMINATED           3   adam            tanh                                  128                 64                  5          0.00473849          83        1            258.659         0.331944 │
│ trial_d8531    TERMINATED           3   adam            tanh                                   32                128                  5          0.00225539          99        1            431.963         0.338495 │
│ trial_d8531    TERMINATED           2   rmsprop         relu                                   64                 64                  3          0.00308065         106        1            231.02          0.372047 │
│ trial_d8531    TERMINATED           4   rmsprop         tanh                                  128                128                  5          0.00244001         129        1            530.461         0.337701 │
│ trial_d8531    TERMINATED           3   adam            tanh                                   32                 64                  5          0.00455572          99        1            442.457         0.304943 │
│ trial_d8531    TERMINATED           3   adam            tanh                                   32                 64                  5          0.00361478         114        1            385.263         0.306532 │
│ trial_d8531    TERMINATED           4   rmsprop         relu                                   32                 32                  5          0.00235051         122        1            480.512         0.318245 │
│ trial_d8531    TERMINATED           4   adam            relu                                  128                 32                  3          8.42402e-05         67        1            507.142         0.229105 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=1648203)[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=1648203)[0m   _log_deprecation_warning(
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[36m(train_cnn_ray_tune pid=1648203)[0m 
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Trial trial_d8531 finished iteration 1 at 2025-10-28 12:42:57. Total running time: 10min 2s
╭──────────────────────────────────────╮
│ Trial trial_d8531 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             599.904 │
│ time_total_s                 599.904 │
│ training_iteration                 1 │
│ val_accuracy                 0.27695 │
╰──────────────────────────────────────╯

Trial trial_d8531 completed after 1 iterations at 2025-10-28 12:42:57. Total running time: 10min 2s
[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648250)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648227)[0m 
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[36m(train_cnn_ray_tune pid=1648227)[0m Epoch 73/81[32m [repeated 11x across cluster][0m
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[36m(train_cnn_ray_tune pid=1648240)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648250)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m Epoch 52/93[32m [repeated 11x across cluster][0m
[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648250)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648194)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m Epoch 98/127[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648221)[0m 
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[36m(train_cnn_ray_tune pid=1648221)[0m Epoch 36/54[32m [repeated 10x across cluster][0m
[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648227)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648240)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648227)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648195)[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=1648195)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=1648205)[0m 
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Trial trial_d8531 finished iteration 1 at 2025-10-28 12:43:22. Total running time: 10min 27s
╭──────────────────────────────────────╮
│ Trial trial_d8531 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             624.734 │
│ time_total_s                 624.734 │
│ training_iteration                 1 │
│ val_accuracy                 0.33532 │
╰──────────────────────────────────────╯

Trial trial_d8531 completed after 1 iterations at 2025-10-28 12:43:22. Total running time: 10min 27s
[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648240)[0m 
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[36m(train_cnn_ray_tune pid=1648194)[0m 
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Trial status: 9 RUNNING | 11 TERMINATED
Current time: 2025-10-28 12:43:26. Total running time: 10min 31s
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_d8531    RUNNING              3   rmsprop         tanh                                   32                 64                  5          9.38039e-05         66                                              │
│ trial_d8531    RUNNING              4   rmsprop         tanh                                  128                 32                  3          2.75956e-05        127                                              │
│ trial_d8531    RUNNING              2   adam            tanh                                   32                 32                  3          5.99053e-05         93                                              │
│ trial_d8531    RUNNING              3   adam            tanh                                   32                 64                  5          2.55695e-05         54                                              │
│ trial_d8531    RUNNING              2   adam            tanh                                  128                128                  3          6.55104e-05         81                                              │
│ trial_d8531    RUNNING              3   rmsprop         relu                                   32                 64                  3          0.000151011         91                                              │
│ trial_d8531    RUNNING              4   adam            relu                                  128                128                  5          0.000175915        124                                              │
│ trial_d8531    RUNNING              4   adam            relu                                  128                 32                  3          1.96033e-05        102                                              │
│ trial_d8531    RUNNING              3   rmsprop         relu                                   64                 32                  3          9.27852e-05         92                                              │
│ trial_d8531    TERMINATED           4   rmsprop         tanh                                   32                128                  5          0.00287631          87        1            327.103         0.30276  │
│ trial_d8531    TERMINATED           3   adam            tanh                                  128                128                  5          9.43275e-05         83        1            624.734         0.335319 │
│ trial_d8531    TERMINATED           3   adam            tanh                                  128                 64                  5          0.00473849          83        1            258.659         0.331944 │
│ trial_d8531    TERMINATED           3   adam            tanh                                   32                128                  5          0.00225539          99        1            431.963         0.338495 │
│ trial_d8531    TERMINATED           2   rmsprop         relu                                   64                 64                  3          0.00308065         106        1            231.02          0.372047 │
│ trial_d8531    TERMINATED           4   rmsprop         tanh                                  128                128                  5          0.00244001         129        1            530.461         0.337701 │
│ trial_d8531    TERMINATED           3   adam            tanh                                   32                 64                  5          0.00455572          99        1            442.457         0.304943 │
│ trial_d8531    TERMINATED           3   adam            tanh                                   32                 64                  5          0.00361478         114        1            385.263         0.306532 │
│ trial_d8531    TERMINATED           4   rmsprop         relu                                   32                 32                  5          0.00235051         122        1            480.512         0.318245 │
│ trial_d8531    TERMINATED           4   adam            relu                                  128                 32                  3          8.42402e-05         67        1            507.142         0.229105 │
│ trial_d8531    TERMINATED           2   rmsprop         tanh                                   64                 32                  5          4.95152e-05         85        1            599.904         0.276951 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648240)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648240)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m Epoch 103/127[32m [repeated 10x across cluster][0m
[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648240)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m Epoch 105/127[32m [repeated 13x across cluster][0m
[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648227)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648227)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648227)[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=1648227)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648227)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648227)[0m 
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Trial trial_d8531 finished iteration 1 at 2025-10-28 12:43:36. Total running time: 10min 41s
╭──────────────────────────────────────╮
│ Trial trial_d8531 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             638.412 │
│ time_total_s                 638.412 │
│ training_iteration                 1 │
│ val_accuracy                 0.30177 │
╰──────────────────────────────────────╯

Trial trial_d8531 completed after 1 iterations at 2025-10-28 12:43:36. Total running time: 10min 41s
[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648240)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648240)[0m 
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[36m(train_cnn_ray_tune pid=1648250)[0m 
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[36m(train_cnn_ray_tune pid=1648250)[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=1648250)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648250)[0m 
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Trial trial_d8531 finished iteration 1 at 2025-10-28 12:43:40. Total running time: 10min 46s
╭──────────────────────────────────────╮
│ Trial trial_d8531 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             642.987 │
│ time_total_s                 642.987 │
│ training_iteration                 1 │
│ val_accuracy                 0.20588 │
╰──────────────────────────────────────╯

Trial trial_d8531 completed after 1 iterations at 2025-10-28 12:43:40. Total running time: 10min 46s
[36m(train_cnn_ray_tune pid=1648250)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648240)[0m 
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[36m(train_cnn_ray_tune pid=1648221)[0m 
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[36m(train_cnn_ray_tune pid=1648240)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m Epoch 50/124[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=1648194)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648221)[0m 
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[36m(train_cnn_ray_tune pid=1648240)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m Epoch 112/127[32m [repeated 10x across cluster][0m
[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m Epoch 61/93[32m [repeated 10x across cluster][0m
[36m(train_cnn_ray_tune pid=1648240)[0m 
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[36m(train_cnn_ray_tune pid=1648240)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648221)[0m 
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Trial status: 7 RUNNING | 13 TERMINATED
Current time: 2025-10-28 12:43:56. Total running time: 11min 1s
Logical resource usage: 7.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_d8531    RUNNING              3   rmsprop         tanh                                   32                 64                  5          9.38039e-05         66                                              │
│ trial_d8531    RUNNING              4   rmsprop         tanh                                  128                 32                  3          2.75956e-05        127                                              │
│ trial_d8531    RUNNING              2   adam            tanh                                   32                 32                  3          5.99053e-05         93                                              │
│ trial_d8531    RUNNING              3   adam            tanh                                   32                 64                  5          2.55695e-05         54                                              │
│ trial_d8531    RUNNING              3   rmsprop         relu                                   32                 64                  3          0.000151011         91                                              │
│ trial_d8531    RUNNING              4   adam            relu                                  128                128                  5          0.000175915        124                                              │
│ trial_d8531    RUNNING              3   rmsprop         relu                                   64                 32                  3          9.27852e-05         92                                              │
│ trial_d8531    TERMINATED           4   rmsprop         tanh                                   32                128                  5          0.00287631          87        1            327.103         0.30276  │
│ trial_d8531    TERMINATED           3   adam            tanh                                  128                128                  5          9.43275e-05         83        1            624.734         0.335319 │
│ trial_d8531    TERMINATED           3   adam            tanh                                  128                 64                  5          0.00473849          83        1            258.659         0.331944 │
│ trial_d8531    TERMINATED           3   adam            tanh                                   32                128                  5          0.00225539          99        1            431.963         0.338495 │
│ trial_d8531    TERMINATED           2   adam            tanh                                  128                128                  3          6.55104e-05         81        1            638.412         0.301767 │
│ trial_d8531    TERMINATED           2   rmsprop         relu                                   64                 64                  3          0.00308065         106        1            231.02          0.372047 │
│ trial_d8531    TERMINATED           4   adam            relu                                  128                 32                  3          1.96033e-05        102        1            642.987         0.205877 │
│ trial_d8531    TERMINATED           4   rmsprop         tanh                                  128                128                  5          0.00244001         129        1            530.461         0.337701 │
│ trial_d8531    TERMINATED           3   adam            tanh                                   32                 64                  5          0.00455572          99        1            442.457         0.304943 │
│ trial_d8531    TERMINATED           3   adam            tanh                                   32                 64                  5          0.00361478         114        1            385.263         0.306532 │
│ trial_d8531    TERMINATED           4   rmsprop         relu                                   32                 32                  5          0.00235051         122        1            480.512         0.318245 │
│ trial_d8531    TERMINATED           4   adam            relu                                  128                 32                  3          8.42402e-05         67        1            507.142         0.229105 │
│ trial_d8531    TERMINATED           2   rmsprop         tanh                                   64                 32                  5          4.95152e-05         85        1            599.904         0.276951 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648240)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m Epoch 117/127[32m [repeated 11x across cluster][0m
[36m(train_cnn_ray_tune pid=1648247)[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=1648247)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648247)[0m 
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Trial trial_d8531 finished iteration 1 at 2025-10-28 12:44:03. Total running time: 11min 8s
╭─────────────────────────────────────╮
│ Trial trial_d8531 result            │
├─────────────────────────────────────┤
│ checkpoint_dir_name                 │
│ time_this_iter_s             665.88 │
│ time_total_s                 665.88 │
│ training_iteration                1 │
│ val_accuracy                 0.3514 │
╰─────────────────────────────────────╯

Trial trial_d8531 completed after 1 iterations at 2025-10-28 12:44:03. Total running time: 11min 8s
[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m Epoch 120/127[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=1648194)[0m 
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[36m(train_cnn_ray_tune pid=1648240)[0m 
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[36m(train_cnn_ray_tune pid=1648240)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648221)[0m 
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[36m(train_cnn_ray_tune pid=1648240)[0m 
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[36m(train_cnn_ray_tune pid=1648240)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648240)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m Epoch 123/127[32m [repeated 10x across cluster][0m
[36m(train_cnn_ray_tune pid=1648240)[0m 
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[36m(train_cnn_ray_tune pid=1648240)[0m 
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Trial trial_d8531 finished iteration 1 at 2025-10-28 12:44:08. Total running time: 11min 14s
[36m(train_cnn_ray_tune pid=1648240)[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=1648240)[0m   _log_deprecation_warning(
╭──────────────────────────────────────╮
│ Trial trial_d8531 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s              671.03 │
│ time_total_s                  671.03 │
│ training_iteration                 1 │
│ val_accuracy                 0.31308 │
╰──────────────────────────────────────╯

Trial trial_d8531 completed after 1 iterations at 2025-10-28 12:44:08. Total running time: 11min 14s
[36m(train_cnn_ray_tune pid=1648194)[0m 
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[36m(train_cnn_ray_tune pid=1648240)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648194)[0m 
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[36m(train_cnn_ray_tune pid=1648221)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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Trial trial_d8531 finished iteration 1 at 2025-10-28 12:44:11. Total running time: 11min 16s
╭──────────────────────────────────────╮
│ Trial trial_d8531 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             673.671 │
│ time_total_s                 673.671 │
│ training_iteration                 1 │
│ val_accuracy                 0.40758 │
╰──────────────────────────────────────╯

Trial trial_d8531 completed after 1 iterations at 2025-10-28 12:44:11. Total running time: 11min 16s
[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[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=1648196)[0m   _log_deprecation_warning([32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m Epoch 67/93[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=1648212)[0m 
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[36m(train_cnn_ray_tune pid=1648221)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648221)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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Trial trial_d8531 finished iteration 1 at 2025-10-28 12:44:16. Total running time: 11min 21s
╭──────────────────────────────────────╮
│ Trial trial_d8531 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             678.491 │
│ time_total_s                 678.491 │
│ training_iteration                 1 │
│ val_accuracy                 0.20985 │
╰──────────────────────────────────────╯

Trial trial_d8531 completed after 1 iterations at 2025-10-28 12:44:16. Total running time: 11min 21s
[36m(train_cnn_ray_tune pid=1648194)[0m 
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[36m(train_cnn_ray_tune pid=1648221)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648196)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m Epoch 69/93[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=1648196)[0m 
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Trial trial_d8531 finished iteration 1 at 2025-10-28 12:44:19. Total running time: 11min 25s
╭──────────────────────────────────────╮
│ Trial trial_d8531 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             682.056 │
│ time_total_s                 682.056 │
│ training_iteration                 1 │
│ val_accuracy                 0.31844 │
╰──────────────────────────────────────╯

Trial trial_d8531 completed after 1 iterations at 2025-10-28 12:44:19. Total running time: 11min 25s
[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648221)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648221)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648194)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m Epoch 72/93[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=1648194)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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Trial status: 18 TERMINATED | 2 RUNNING
Current time: 2025-10-28 12:44:26. Total running time: 11min 31s
Logical resource usage: 2.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_d8531    RUNNING              2   adam            tanh                                   32                 32                  3          5.99053e-05         93                                              │
│ trial_d8531    RUNNING              3   adam            tanh                                   32                 64                  5          2.55695e-05         54                                              │
│ trial_d8531    TERMINATED           3   rmsprop         tanh                                   32                 64                  5          9.38039e-05         66        1            682.056         0.318444 │
│ trial_d8531    TERMINATED           4   rmsprop         tanh                                   32                128                  5          0.00287631          87        1            327.103         0.30276  │
│ trial_d8531    TERMINATED           3   adam            tanh                                  128                128                  5          9.43275e-05         83        1            624.734         0.335319 │
│ trial_d8531    TERMINATED           3   adam            tanh                                  128                 64                  5          0.00473849          83        1            258.659         0.331944 │
│ trial_d8531    TERMINATED           4   rmsprop         tanh                                  128                 32                  3          2.75956e-05        127        1            678.491         0.209847 │
│ trial_d8531    TERMINATED           3   adam            tanh                                   32                128                  5          0.00225539          99        1            431.963         0.338495 │
│ trial_d8531    TERMINATED           2   adam            tanh                                  128                128                  3          6.55104e-05         81        1            638.412         0.301767 │
│ trial_d8531    TERMINATED           3   rmsprop         relu                                   32                 64                  3          0.000151011         91        1            665.88          0.3514   │
│ trial_d8531    TERMINATED           2   rmsprop         relu                                   64                 64                  3          0.00308065         106        1            231.02          0.372047 │
│ trial_d8531    TERMINATED           4   adam            relu                                  128                128                  5          0.000175915        124        1            673.671         0.407584 │
│ trial_d8531    TERMINATED           4   adam            relu                                  128                 32                  3          1.96033e-05        102        1            642.987         0.205877 │
│ trial_d8531    TERMINATED           4   rmsprop         tanh                                  128                128                  5          0.00244001         129        1            530.461         0.337701 │
│ trial_d8531    TERMINATED           3   adam            tanh                                   32                 64                  5          0.00455572          99        1            442.457         0.304943 │
│ trial_d8531    TERMINATED           3   adam            tanh                                   32                 64                  5          0.00361478         114        1            385.263         0.306532 │
│ trial_d8531    TERMINATED           4   rmsprop         relu                                   32                 32                  5          0.00235051         122        1            480.512         0.318245 │
│ trial_d8531    TERMINATED           3   rmsprop         relu                                   64                 32                  3          9.27852e-05         92        1            671.03          0.313083 │
│ trial_d8531    TERMINATED           4   adam            relu                                  128                 32                  3          8.42402e-05         67        1            507.142         0.229105 │
│ trial_d8531    TERMINATED           2   rmsprop         tanh                                   64                 32                  5          4.95152e-05         85        1            599.904         0.276951 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m Epoch 75/93[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648221)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m Epoch 78/93[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648221)[0m 
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Trial trial_d8531 finished iteration 1 at 2025-10-28 12:44:37. Total running time: 11min 42s
[36m(train_cnn_ray_tune pid=1648221)[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=1648221)[0m   _log_deprecation_warning([32m [repeated 2x across cluster][0m
╭──────────────────────────────────────╮
│ Trial trial_d8531 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             699.753 │
│ time_total_s                 699.753 │
│ training_iteration                 1 │
│ val_accuracy                 0.29045 │
╰──────────────────────────────────────╯

Trial trial_d8531 completed after 1 iterations at 2025-10-28 12:44:37. Total running time: 11min 42s
[36m(train_cnn_ray_tune pid=1648221)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648221)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m Epoch 81/93[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m Epoch 85/93[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m Epoch 89/93[32m [repeated 4x across cluster][0m
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m Epoch 92/93[32m [repeated 3x across cluster][0m
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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Trial status: 19 TERMINATED | 1 RUNNING
Current time: 2025-10-28 12:44:56. Total running time: 12min 1s
Logical resource usage: 1.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_d8531    RUNNING              2   adam            tanh                                   32                 32                  3          5.99053e-05         93                                              │
│ trial_d8531    TERMINATED           3   rmsprop         tanh                                   32                 64                  5          9.38039e-05         66        1            682.056         0.318444 │
│ trial_d8531    TERMINATED           4   rmsprop         tanh                                   32                128                  5          0.00287631          87        1            327.103         0.30276  │
│ trial_d8531    TERMINATED           3   adam            tanh                                  128                128                  5          9.43275e-05         83        1            624.734         0.335319 │
│ trial_d8531    TERMINATED           3   adam            tanh                                  128                 64                  5          0.00473849          83        1            258.659         0.331944 │
│ trial_d8531    TERMINATED           4   rmsprop         tanh                                  128                 32                  3          2.75956e-05        127        1            678.491         0.209847 │
│ trial_d8531    TERMINATED           3   adam            tanh                                   32                 64                  5          2.55695e-05         54        1            699.753         0.290451 │
│ trial_d8531    TERMINATED           3   adam            tanh                                   32                128                  5          0.00225539          99        1            431.963         0.338495 │
│ trial_d8531    TERMINATED           2   adam            tanh                                  128                128                  3          6.55104e-05         81        1            638.412         0.301767 │
│ trial_d8531    TERMINATED           3   rmsprop         relu                                   32                 64                  3          0.000151011         91        1            665.88          0.3514   │
│ trial_d8531    TERMINATED           2   rmsprop         relu                                   64                 64                  3          0.00308065         106        1            231.02          0.372047 │
│ trial_d8531    TERMINATED           4   adam            relu                                  128                128                  5          0.000175915        124        1            673.671         0.407584 │
│ trial_d8531    TERMINATED           4   adam            relu                                  128                 32                  3          1.96033e-05        102        1            642.987         0.205877 │
│ trial_d8531    TERMINATED           4   rmsprop         tanh                                  128                128                  5          0.00244001         129        1            530.461         0.337701 │
│ trial_d8531    TERMINATED           3   adam            tanh                                   32                 64                  5          0.00455572          99        1            442.457         0.304943 │
│ trial_d8531    TERMINATED           3   adam            tanh                                   32                 64                  5          0.00361478         114        1            385.263         0.306532 │
│ trial_d8531    TERMINATED           4   rmsprop         relu                                   32                 32                  5          0.00235051         122        1            480.512         0.318245 │
│ trial_d8531    TERMINATED           3   rmsprop         relu                                   64                 32                  3          9.27852e-05         92        1            671.03          0.313083 │
│ trial_d8531    TERMINATED           4   adam            relu                                  128                 32                  3          8.42402e-05         67        1            507.142         0.229105 │
│ trial_d8531    TERMINATED           2   rmsprop         tanh                                   64                 32                  5          4.95152e-05         85        1            599.904         0.276951 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
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[36m(train_cnn_ray_tune pid=1648205)[0m 
[1m156/158[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 982us/step
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[36m(train_cnn_ray_tune pid=1648205)[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=1648205)[0m   _log_deprecation_warning(
2025-10-28 12:44:56,661	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_gyr_17_classes/ESANN_hyperparameters_tuning' in 0.0057s.
/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 00:00:1761651896.799914 1646624 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13749 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
Trial trial_d8531 finished iteration 1 at 2025-10-28 12:44:56. Total running time: 12min 2s
╭──────────────────────────────────────╮
│ Trial trial_d8531 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             719.127 │
│ time_total_s                 719.127 │
│ training_iteration                 1 │
│ val_accuracy                 0.24697 │
╰──────────────────────────────────────╯

Trial trial_d8531 completed after 1 iterations at 2025-10-28 12:44:56. Total running time: 12min 2s

Trial status: 20 TERMINATED
Current time: 2025-10-28 12:44:56. Total running time: 12min 2s
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_d8531    TERMINATED           3   rmsprop         tanh                                   32                 64                  5          9.38039e-05         66        1            682.056         0.318444 │
│ trial_d8531    TERMINATED           4   rmsprop         tanh                                   32                128                  5          0.00287631          87        1            327.103         0.30276  │
│ trial_d8531    TERMINATED           3   adam            tanh                                  128                128                  5          9.43275e-05         83        1            624.734         0.335319 │
│ trial_d8531    TERMINATED           3   adam            tanh                                  128                 64                  5          0.00473849          83        1            258.659         0.331944 │
│ trial_d8531    TERMINATED           4   rmsprop         tanh                                  128                 32                  3          2.75956e-05        127        1            678.491         0.209847 │
│ trial_d8531    TERMINATED           2   adam            tanh                                   32                 32                  3          5.99053e-05         93        1            719.127         0.246972 │
│ trial_d8531    TERMINATED           3   adam            tanh                                   32                 64                  5          2.55695e-05         54        1            699.753         0.290451 │
│ trial_d8531    TERMINATED           3   adam            tanh                                   32                128                  5          0.00225539          99        1            431.963         0.338495 │
│ trial_d8531    TERMINATED           2   adam            tanh                                  128                128                  3          6.55104e-05         81        1            638.412         0.301767 │
│ trial_d8531    TERMINATED           3   rmsprop         relu                                   32                 64                  3          0.000151011         91        1            665.88          0.3514   │
│ trial_d8531    TERMINATED           2   rmsprop         relu                                   64                 64                  3          0.00308065         106        1            231.02          0.372047 │
│ trial_d8531    TERMINATED           4   adam            relu                                  128                128                  5          0.000175915        124        1            673.671         0.407584 │
│ trial_d8531    TERMINATED           4   adam            relu                                  128                 32                  3          1.96033e-05        102        1            642.987         0.205877 │
│ trial_d8531    TERMINATED           4   rmsprop         tanh                                  128                128                  5          0.00244001         129        1            530.461         0.337701 │
│ trial_d8531    TERMINATED           3   adam            tanh                                   32                 64                  5          0.00455572          99        1            442.457         0.304943 │
│ trial_d8531    TERMINATED           3   adam            tanh                                   32                 64                  5          0.00361478         114        1            385.263         0.306532 │
│ trial_d8531    TERMINATED           4   rmsprop         relu                                   32                 32                  5          0.00235051         122        1            480.512         0.318245 │
│ trial_d8531    TERMINATED           3   rmsprop         relu                                   64                 32                  3          9.27852e-05         92        1            671.03          0.313083 │
│ trial_d8531    TERMINATED           4   adam            relu                                  128                 32                  3          8.42402e-05         67        1            507.142         0.229105 │
│ trial_d8531    TERMINATED           2   rmsprop         tanh                                   64                 32                  5          4.95152e-05         85        1            599.904         0.276951 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

Mejores hiperparámetros: {'N_capas': 4, 'optimizador': 'adam', 'funcion_activacion': 'relu', 'tamanho_minilote': 128, 'numero_filtros': 128, 'tamanho_filtro': 5, 'tasa_aprendizaje': 0.0001759154873714111, 'epochs': 124}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 409,231 (1.56 MB)
 Trainable params: 409,231 (1.56 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/124
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761651899.447786 1752952 service.cc:152] XLA service 0x7374a4003110 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761651899.447874 1752952 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-28 12:44:59.506029: 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 00:00:1761651899.822783 1752952 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761651903.582168 1752952 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m14:23[0m 6s/step - accuracy: 0.0703 - loss: 3.2738
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Epoch 2/124

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Epoch 3/124

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Epoch 4/124

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[1m104/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1278 - loss: 2.8081
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Epoch 5/124

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[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1375 - loss: 2.7244
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Epoch 6/124

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[1m104/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1461 - loss: 2.6744
[1m131/145[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.1469 - loss: 2.6709
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.1474 - loss: 2.6685 - val_accuracy: 0.2505 - val_loss: 2.3075
Epoch 7/124

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[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1572 - loss: 2.6110
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Epoch 8/124

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Epoch 9/124

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Epoch 10/124

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[1m123/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1882 - loss: 2.4796
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.1886 - loss: 2.4799
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Epoch 11/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 25ms/step - accuracy: 0.2109 - loss: 2.4301
[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1957 - loss: 2.4698 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1961 - loss: 2.4697
[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1963 - loss: 2.4674
[1m104/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1959 - loss: 2.4668
[1m128/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1954 - loss: 2.4658
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.1951 - loss: 2.4649 - val_accuracy: 0.3105 - val_loss: 2.2040
Epoch 12/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2193 - loss: 2.3883 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2161 - loss: 2.3968
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2145 - loss: 2.4015
[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2134 - loss: 2.4049
[1m128/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2123 - loss: 2.4075
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2118 - loss: 2.4088 - val_accuracy: 0.3238 - val_loss: 2.1969
Epoch 13/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2031 - loss: 2.3315
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2057 - loss: 2.4088 
[1m 53/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2096 - loss: 2.4073
[1m 79/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2125 - loss: 2.4027
[1m105/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2137 - loss: 2.4004
[1m130/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2145 - loss: 2.3987
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2148 - loss: 2.3983 - val_accuracy: 0.3300 - val_loss: 2.1826
Epoch 14/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.1797 - loss: 2.3196
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2038 - loss: 2.4031 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2107 - loss: 2.3924
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2136 - loss: 2.3894
[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2159 - loss: 2.3866
[1m127/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2170 - loss: 2.3841
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2174 - loss: 2.3828 - val_accuracy: 0.3379 - val_loss: 2.1646
Epoch 15/124

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[1m 97/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2161 - loss: 2.3762
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Epoch 16/124

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[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2360 - loss: 2.3405
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Epoch 17/124

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[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2387 - loss: 2.3094
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2379 - loss: 2.3088
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Epoch 18/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2348 - loss: 2.3018 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2362 - loss: 2.3021
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[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2387 - loss: 2.2972
[1m128/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2398 - loss: 2.2954
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2403 - loss: 2.2945 - val_accuracy: 0.3532 - val_loss: 2.1417
Epoch 19/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.2734 - loss: 2.2200
[1m 23/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2506 - loss: 2.3043 
[1m 46/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2470 - loss: 2.3039
[1m 70/145[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2460 - loss: 2.2991
[1m 96/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2454 - loss: 2.2956
[1m121/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2452 - loss: 2.2934
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2453 - loss: 2.2912 - val_accuracy: 0.3621 - val_loss: 2.1227
Epoch 20/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.1719 - loss: 2.3788
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2326 - loss: 2.3038 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2400 - loss: 2.2920
[1m 75/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2436 - loss: 2.2814
[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2454 - loss: 2.2763
[1m126/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2459 - loss: 2.2747
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2463 - loss: 2.2736 - val_accuracy: 0.3500 - val_loss: 2.1371
Epoch 21/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.2578 - loss: 2.3925
[1m 24/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2558 - loss: 2.2526 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2582 - loss: 2.2443
[1m 75/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2590 - loss: 2.2428
[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2588 - loss: 2.2426
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2582 - loss: 2.2435
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2578 - loss: 2.2441 - val_accuracy: 0.3540 - val_loss: 2.1407
Epoch 22/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2601 - loss: 2.2493 
[1m 48/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2626 - loss: 2.2386
[1m 74/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2631 - loss: 2.2343
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2630 - loss: 2.2329
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2627 - loss: 2.2321
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2625 - loss: 2.2316 - val_accuracy: 0.3476 - val_loss: 2.1293
Epoch 23/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2031 - loss: 2.4094
[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2550 - loss: 2.2643 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2581 - loss: 2.2446
[1m 74/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2592 - loss: 2.2391
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2602 - loss: 2.2346
[1m127/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2612 - loss: 2.2305
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2615 - loss: 2.2285 - val_accuracy: 0.3631 - val_loss: 2.1068
Epoch 24/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 28ms/step - accuracy: 0.3125 - loss: 2.1383
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2618 - loss: 2.2126 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2597 - loss: 2.2161
[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2609 - loss: 2.2115
[1m104/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2613 - loss: 2.2107
[1m129/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2621 - loss: 2.2093
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2627 - loss: 2.2081 - val_accuracy: 0.3623 - val_loss: 2.1075
Epoch 25/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2812 - loss: 2.1797
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2769 - loss: 2.1907 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2708 - loss: 2.2002
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2707 - loss: 2.1978
[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2715 - loss: 2.1934
[1m126/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2725 - loss: 2.1903
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2732 - loss: 2.1888 - val_accuracy: 0.3560 - val_loss: 2.1036
Epoch 26/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.3125 - loss: 2.1081
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2890 - loss: 2.1505 
[1m 48/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2829 - loss: 2.1583
[1m 72/145[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2796 - loss: 2.1631
[1m 94/145[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2781 - loss: 2.1674
[1m119/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2776 - loss: 2.1683
[1m142/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.2778 - loss: 2.1679
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2779 - loss: 2.1679 - val_accuracy: 0.3679 - val_loss: 2.0907
Epoch 27/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.3281 - loss: 2.0758
[1m 24/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2858 - loss: 2.1394 
[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2821 - loss: 2.1409
[1m 74/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2820 - loss: 2.1429
[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2819 - loss: 2.1453
[1m123/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2818 - loss: 2.1472
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2816 - loss: 2.1486 - val_accuracy: 0.3580 - val_loss: 2.1040
Epoch 28/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.3047 - loss: 2.0816
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2874 - loss: 2.1298 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2886 - loss: 2.1340
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2900 - loss: 2.1354
[1m104/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2897 - loss: 2.1383
[1m128/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2887 - loss: 2.1403
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2883 - loss: 2.1412 - val_accuracy: 0.3609 - val_loss: 2.0820
Epoch 29/124

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[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2783 - loss: 2.1429 
[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2825 - loss: 2.1375
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Epoch 30/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3004 - loss: 2.1043 
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[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2982 - loss: 2.1186
[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2974 - loss: 2.1199
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Epoch 31/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2975 - loss: 2.1243 
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[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2986 - loss: 2.1154
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Epoch 32/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3054 - loss: 2.0875 
[1m 53/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3041 - loss: 2.0931
[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3031 - loss: 2.0942
[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3025 - loss: 2.0950
[1m128/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3021 - loss: 2.0949
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3018 - loss: 2.0954 - val_accuracy: 0.3613 - val_loss: 2.0801
Epoch 33/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2967 - loss: 2.0941 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2968 - loss: 2.0912
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2972 - loss: 2.0926
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2976 - loss: 2.0932
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2983 - loss: 2.0932
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2989 - loss: 2.0930 - val_accuracy: 0.3720 - val_loss: 2.0756
Epoch 34/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 26ms/step - accuracy: 0.2891 - loss: 2.1700
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2952 - loss: 2.0988 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3011 - loss: 2.0789
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3019 - loss: 2.0750
[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3027 - loss: 2.0753
[1m123/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3031 - loss: 2.0767
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3034 - loss: 2.0779
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3034 - loss: 2.0780 - val_accuracy: 0.3724 - val_loss: 2.0722
Epoch 35/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3282 - loss: 2.0611 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3221 - loss: 2.0628
[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3184 - loss: 2.0651
[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3160 - loss: 2.0666
[1m127/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3142 - loss: 2.0679
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3132 - loss: 2.0685 - val_accuracy: 0.3838 - val_loss: 2.0610
Epoch 36/124

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[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3172 - loss: 2.0434
[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3159 - loss: 2.0465
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Epoch 37/124

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[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3079 - loss: 2.0497
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Epoch 38/124

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[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3185 - loss: 2.0261
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[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3178 - loss: 2.0304
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Epoch 39/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3190 - loss: 2.0424 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3148 - loss: 2.0457
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3141 - loss: 2.0457
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3143 - loss: 2.0449
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3147 - loss: 2.0437
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Epoch 40/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3251 - loss: 2.0231 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3235 - loss: 2.0254
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3227 - loss: 2.0274
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3225 - loss: 2.0277
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3220 - loss: 2.0275
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3217 - loss: 2.0272 - val_accuracy: 0.3738 - val_loss: 2.0375
Epoch 41/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2578 - loss: 2.1291
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3222 - loss: 2.0081 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3200 - loss: 2.0139
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3191 - loss: 2.0147
[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3189 - loss: 2.0138
[1m127/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3192 - loss: 2.0126
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3195 - loss: 2.0121 - val_accuracy: 0.3911 - val_loss: 2.0233
Epoch 42/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3039 - loss: 2.0340 
[1m 53/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3143 - loss: 2.0144
[1m 79/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3184 - loss: 2.0086
[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3209 - loss: 2.0061
[1m126/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3224 - loss: 2.0041
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Epoch 43/124

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Epoch 44/124

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Epoch 45/124

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Epoch 46/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3254 - loss: 2.0082 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3262 - loss: 2.0006
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3286 - loss: 1.9944
[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3302 - loss: 1.9896
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Epoch 47/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3413 - loss: 1.9681 
[1m 53/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3410 - loss: 1.9648
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3421 - loss: 1.9641
[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3424 - loss: 1.9652
[1m126/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3423 - loss: 1.9654
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3423 - loss: 1.9650 - val_accuracy: 0.3979 - val_loss: 2.0216
Epoch 48/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3609 - loss: 1.9208 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3541 - loss: 1.9308
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[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3504 - loss: 1.9330
[1m127/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3497 - loss: 1.9347
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3492 - loss: 1.9361 - val_accuracy: 0.3883 - val_loss: 2.0151
Epoch 49/124

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[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3358 - loss: 1.9597
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[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3367 - loss: 1.9548
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Epoch 50/124

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Saved model to disk.
[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m 
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[36m(train_cnn_ray_tune pid=1648205)[0m Epoch 93/93

=== EJECUCIÓN 1 ===

--- TRAIN (ejecución 1) ---

--- TEST (ejecución 1) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 409,231 (1.56 MB)
 Trainable params: 409,231 (1.56 MB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 6, 250)
(18469, 6, 250)

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[1m186/578[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 822us/step
[1m253/578[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 803us/step
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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 2ms/step

[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 17ms/step
[1m55/86[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 938us/step
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step  
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 9ms/step

[1m  1/158[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step
[1m 62/158[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 832us/step
[1m129/158[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 792us/step
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 38.77 [%]
Global F1 score (validation) = 36.42 [%]
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[[0.00082381 0.00043771 0.00070943 ... 0.03419254 0.00152003 0.00038372]
 [0.00136893 0.00107582 0.00112046 ... 0.07275391 0.00266837 0.00049103]
 [0.00069509 0.00031419 0.00060059 ... 0.02615889 0.00127078 0.00033501]
 ...
 [0.17718503 0.04212699 0.20881972 ... 0.00074408 0.24154042 0.08743502]
 [0.18193167 0.06510191 0.18313725 ... 0.00193719 0.15002857 0.1245186 ]
 [0.18078487 0.04402643 0.20773286 ... 0.00079951 0.24320792 0.08093132]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 40.79 [%]
Global accuracy score (test) = 32.88 [%]
Global F1 score (train) = 39.65 [%]
Global F1 score (test) = 32.16 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.28      0.17      0.21       184
 CAMINAR CON MÓVIL O LIBRO       0.35      0.34      0.34       184
       CAMINAR USUAL SPEED       0.17      0.08      0.11       184
            CAMINAR ZIGZAG       0.07      0.04      0.05       184
          DE PIE BARRIENDO       0.30      0.23      0.26       184
   DE PIE DOBLANDO TOALLAS       0.38      0.34      0.36       184
    DE PIE MOVIENDO LIBROS       0.42      0.28      0.34       184
          DE PIE USANDO PC       0.28      0.29      0.29       184
        FASE REPOSO CON K5       0.44      0.74      0.55       184
INCREMENTAL CICLOERGOMETRO       0.49      0.62      0.55       184
           SENTADO LEYENDO       0.45      0.40      0.42       184
         SENTADO USANDO PC       0.14      0.10      0.12       184
      SENTADO VIENDO LA TV       0.38      0.30      0.34       184
   SUBIR Y BAJAR ESCALERAS       0.16      0.43      0.24       184
                    TROTAR       0.75      0.56      0.64       161

                  accuracy                           0.33      2737
                 macro avg       0.34      0.33      0.32      2737
              weighted avg       0.33      0.33      0.32      2737

2025-10-28 12:45:46.207419: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-28 12:45:46.218979: 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 00:00:1761651946.232191 1758585 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761651946.236471 1758585 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761651946.246416 1758585 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761651946.246438 1758585 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761651946.246441 1758585 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761651946.246444 1758585 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-28 12:45:46.250388: 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 00:00:1761651948.632421 1758585 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13749 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 409,231 (1.56 MB)
 Trainable params: 409,231 (1.56 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/124
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761651951.210049 1758680 service.cc:152] XLA service 0x74eae8011fc0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761651951.210120 1758680 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-28 12:45:51.267855: 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 00:00:1761651951.576881 1758680 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761651955.189877 1758680 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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
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Epoch 2/124

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Epoch 3/124

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Epoch 4/124

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[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1266 - loss: 2.8247
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[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.1271 - loss: 2.8173 - val_accuracy: 0.2154 - val_loss: 2.3860
Epoch 5/124

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[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1343 - loss: 2.7270
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.1346 - loss: 2.7252 - val_accuracy: 0.2299 - val_loss: 2.3522
Epoch 6/124

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Epoch 7/124

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Epoch 8/124

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Epoch 9/124

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[1m 47/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1704 - loss: 2.5522
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[1m 97/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1706 - loss: 2.5508
[1m122/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1713 - loss: 2.5490
[1m144/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.1721 - loss: 2.5469
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Epoch 10/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1668 - loss: 2.5684 
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[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1765 - loss: 2.5414
[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1789 - loss: 2.5345
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1803 - loss: 2.5293
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.1809 - loss: 2.5267
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.1810 - loss: 2.5266 - val_accuracy: 0.2857 - val_loss: 2.2793
Epoch 11/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2344 - loss: 2.4688
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2087 - loss: 2.4559 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2049 - loss: 2.4597
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2013 - loss: 2.4640
[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1986 - loss: 2.4675
[1m129/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1970 - loss: 2.4682
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.1963 - loss: 2.4683 - val_accuracy: 0.2952 - val_loss: 2.2459
Epoch 12/124

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[1m 24/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1905 - loss: 2.4686 
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[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1969 - loss: 2.4528
[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1975 - loss: 2.4502
[1m129/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1983 - loss: 2.4470
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.1988 - loss: 2.4452 - val_accuracy: 0.3004 - val_loss: 2.2487
Epoch 13/124

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Epoch 14/124

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[1m104/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2062 - loss: 2.4086
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Epoch 15/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2127 - loss: 2.3875 
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[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2197 - loss: 2.3786
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[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2201 - loss: 2.3767 - val_accuracy: 0.3272 - val_loss: 2.1901
Epoch 16/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2311 - loss: 2.3581 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2314 - loss: 2.3562
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2313 - loss: 2.3574
[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2308 - loss: 2.3583
[1m128/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2301 - loss: 2.3571
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2300 - loss: 2.3560 - val_accuracy: 0.3345 - val_loss: 2.1928
Epoch 17/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2419 - loss: 2.3119 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2412 - loss: 2.3209
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2411 - loss: 2.3251
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2398 - loss: 2.3272
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2391 - loss: 2.3272
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2386 - loss: 2.3268 - val_accuracy: 0.3413 - val_loss: 2.1718
Epoch 18/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 28ms/step - accuracy: 0.2578 - loss: 2.2469
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2429 - loss: 2.2950 
[1m 53/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2405 - loss: 2.3031
[1m 79/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2404 - loss: 2.3038
[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2403 - loss: 2.3028
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2404 - loss: 2.3025
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2403 - loss: 2.3024 - val_accuracy: 0.3548 - val_loss: 2.1443
Epoch 19/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2422 - loss: 2.2214
[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2397 - loss: 2.2689 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2425 - loss: 2.2724
[1m 72/145[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2424 - loss: 2.2752
[1m 97/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2421 - loss: 2.2769
[1m121/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2424 - loss: 2.2771
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2430 - loss: 2.2768 - val_accuracy: 0.3482 - val_loss: 2.1518
Epoch 20/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2398 - loss: 2.2996 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2430 - loss: 2.2951
[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2443 - loss: 2.2914
[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2451 - loss: 2.2882
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[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2460 - loss: 2.2837 - val_accuracy: 0.3450 - val_loss: 2.1549
Epoch 21/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2812 - loss: 2.2730
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2593 - loss: 2.2582 
[1m 53/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2587 - loss: 2.2526
[1m 75/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2587 - loss: 2.2500
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2586 - loss: 2.2493
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2583 - loss: 2.2494
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2581 - loss: 2.2494 - val_accuracy: 0.3514 - val_loss: 2.1577
Epoch 22/124

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[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2597 - loss: 2.2229 
[1m 48/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2578 - loss: 2.2329
[1m 73/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2581 - loss: 2.2356
[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2594 - loss: 2.2360
[1m121/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2606 - loss: 2.2350
[1m142/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.2612 - loss: 2.2345
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2613 - loss: 2.2345 - val_accuracy: 0.3488 - val_loss: 2.1303
Epoch 23/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 25ms/step - accuracy: 0.3281 - loss: 2.2232
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2645 - loss: 2.2522 
[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2635 - loss: 2.2385
[1m 71/145[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2643 - loss: 2.2309
[1m 94/145[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2647 - loss: 2.2278
[1m119/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2648 - loss: 2.2256
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.2649 - loss: 2.2239
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2649 - loss: 2.2238 - val_accuracy: 0.3512 - val_loss: 2.1467
Epoch 24/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2969 - loss: 2.0999
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2806 - loss: 2.1646 
[1m 53/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2743 - loss: 2.1817
[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2722 - loss: 2.1879
[1m104/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2711 - loss: 2.1919
[1m129/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2705 - loss: 2.1950
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2702 - loss: 2.1968 - val_accuracy: 0.3548 - val_loss: 2.1200
Epoch 25/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.2891 - loss: 2.0068
[1m 28/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2620 - loss: 2.1932 
[1m 55/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2587 - loss: 2.2100
[1m 81/145[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2589 - loss: 2.2123
[1m106/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2604 - loss: 2.2112
[1m132/145[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.2623 - loss: 2.2091
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2633 - loss: 2.2077 - val_accuracy: 0.3476 - val_loss: 2.1436
Epoch 26/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.2734 - loss: 2.1755
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2766 - loss: 2.2059 
[1m 53/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2760 - loss: 2.1947
[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2763 - loss: 2.1881
[1m104/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2761 - loss: 2.1854
[1m127/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2758 - loss: 2.1848
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Epoch 27/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2763 - loss: 2.1522 
[1m 53/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2778 - loss: 2.1530
[1m 79/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2779 - loss: 2.1550
[1m106/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2789 - loss: 2.1560
[1m131/145[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.2797 - loss: 2.1567
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Epoch 28/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2868 - loss: 2.1497 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2836 - loss: 2.1553
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[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2821 - loss: 2.1580
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Epoch 29/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2656 - loss: 2.1784
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2951 - loss: 2.1610 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2930 - loss: 2.1559
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2937 - loss: 2.1510
[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2937 - loss: 2.1493
[1m128/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2935 - loss: 2.1487
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2933 - loss: 2.1483 - val_accuracy: 0.3657 - val_loss: 2.0928
Epoch 30/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.2812 - loss: 2.1317
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2922 - loss: 2.1212 
[1m 53/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2919 - loss: 2.1288
[1m 79/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2917 - loss: 2.1322
[1m104/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2922 - loss: 2.1317
[1m130/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2927 - loss: 2.1310
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2926 - loss: 2.1309 - val_accuracy: 0.3637 - val_loss: 2.0897
Epoch 31/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2500 - loss: 2.0811
[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2953 - loss: 2.0966 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2966 - loss: 2.1056
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2962 - loss: 2.1110
[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2954 - loss: 2.1152
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2947 - loss: 2.1180
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2943 - loss: 2.1195 - val_accuracy: 0.3709 - val_loss: 2.0883
Epoch 32/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.2891 - loss: 2.1317
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2889 - loss: 2.1306 
[1m 53/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2905 - loss: 2.1230
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2918 - loss: 2.1188
[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2922 - loss: 2.1178
[1m128/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2926 - loss: 2.1168
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2931 - loss: 2.1161 - val_accuracy: 0.3687 - val_loss: 2.0841
Epoch 33/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2812 - loss: 2.0579
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2952 - loss: 2.1083 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2994 - loss: 2.1070
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3022 - loss: 2.1025
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3038 - loss: 2.1001
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3049 - loss: 2.0981
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3055 - loss: 2.0968 - val_accuracy: 0.3659 - val_loss: 2.0768
Epoch 34/124

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Epoch 35/124

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Epoch 36/124

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Epoch 37/124

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[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3213 - loss: 2.0542
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[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3175 - loss: 2.0563
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3168 - loss: 2.0556
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Epoch 38/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3128 - loss: 2.0830 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3114 - loss: 2.0802
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[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3107 - loss: 2.0718
[1m122/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3106 - loss: 2.0694
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3110 - loss: 2.0669 - val_accuracy: 0.3840 - val_loss: 2.0452
Epoch 39/124

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[1m 24/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3156 - loss: 2.0315 
[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3169 - loss: 2.0380
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[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3187 - loss: 2.0377
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3190 - loss: 2.0370
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3188 - loss: 2.0373 - val_accuracy: 0.3887 - val_loss: 2.0420
Epoch 40/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3376 - loss: 2.0122 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3317 - loss: 2.0232
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[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3292 - loss: 2.0276
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3282 - loss: 2.0291
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Epoch 41/124

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Epoch 42/124

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Epoch 43/124

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Epoch 44/124

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[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3286 - loss: 1.9926
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[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3303 - loss: 1.9900
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3309 - loss: 1.9898
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Epoch 45/124

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[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3404 - loss: 1.9838
[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3387 - loss: 1.9878
[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3379 - loss: 1.9877
[1m126/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3377 - loss: 1.9869
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3375 - loss: 1.9867 - val_accuracy: 0.3790 - val_loss: 2.0224
Epoch 46/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3433 - loss: 1.9730 
[1m 53/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3399 - loss: 1.9813
[1m 79/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3370 - loss: 1.9843
[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3356 - loss: 1.9853
[1m128/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3348 - loss: 1.9853
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3347 - loss: 1.9846 - val_accuracy: 0.4012 - val_loss: 2.0078
Epoch 47/124

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[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3419 - loss: 1.9507 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3388 - loss: 1.9615
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[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3385 - loss: 1.9629
[1m129/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3381 - loss: 1.9639
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Epoch 48/124

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Epoch 49/124

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[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3426 - loss: 1.9444
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[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3436 - loss: 1.9450 - val_accuracy: 0.3975 - val_loss: 2.0100

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[1m62/86[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 832us/step  
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step 
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Saved model to disk.

Accuracy capturado en la ejecución 1: 32.88 [%]
F1-score capturado en la ejecución 1: 32.16 [%]

=== EJECUCIÓN 2 ===

--- TRAIN (ejecución 2) ---

--- TEST (ejecución 2) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 409,231 (1.56 MB)
 Trainable params: 409,231 (1.56 MB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 6, 250)
(18469, 6, 250)

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[1m475/578[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 849us/step
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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 2ms/step

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[1m63/86[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 813us/step
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step  
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 9ms/step

[1m  1/158[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step
[1m 63/158[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 810us/step
[1m133/158[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 764us/step
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 39.75 [%]
Global F1 score (validation) = 38.31 [%]
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[[2.2616889e-03 9.1218681e-04 1.2231256e-03 ... 3.4674417e-02
  2.7106949e-03 6.2408001e-04]
 [1.6689525e-03 9.3726418e-04 1.2752756e-03 ... 5.6591064e-02
  2.2907320e-03 4.7537434e-04]
 [5.5132929e-04 1.6288608e-04 4.1405574e-04 ... 1.7557532e-02
  7.9627318e-04 2.9976998e-04]
 ...
 [1.7075676e-01 4.9757253e-02 2.1209952e-01 ... 8.0641900e-04
  2.2826530e-01 8.8891819e-02]
 [1.7837285e-01 5.4984964e-02 1.9638066e-01 ... 1.3291374e-03
  2.1405470e-01 8.8333465e-02]
 [1.6336843e-01 4.1859932e-02 2.0181179e-01 ... 7.4164558e-04
  2.4357104e-01 9.5688395e-02]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 40.53 [%]
Global accuracy score (test) = 31.31 [%]
Global F1 score (train) = 40.01 [%]
Global F1 score (test) = 31.48 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.23      0.24      0.24       184
 CAMINAR CON MÓVIL O LIBRO       0.35      0.35      0.35       184
       CAMINAR USUAL SPEED       0.09      0.04      0.05       184
            CAMINAR ZIGZAG       0.15      0.18      0.16       184
          DE PIE BARRIENDO       0.29      0.22      0.25       184
   DE PIE DOBLANDO TOALLAS       0.37      0.33      0.34       184
    DE PIE MOVIENDO LIBROS       0.42      0.30      0.35       184
          DE PIE USANDO PC       0.24      0.21      0.22       184
        FASE REPOSO CON K5       0.40      0.74      0.52       184
INCREMENTAL CICLOERGOMETRO       0.59      0.55      0.57       184
           SENTADO LEYENDO       0.40      0.32      0.36       184
         SENTADO USANDO PC       0.15      0.16      0.16       184
      SENTADO VIENDO LA TV       0.36      0.30      0.33       184
   SUBIR Y BAJAR ESCALERAS       0.12      0.24      0.16       184
                    TROTAR       0.81      0.54      0.65       161

                  accuracy                           0.31      2737
                 macro avg       0.33      0.32      0.31      2737
              weighted avg       0.33      0.31      0.31      2737

2025-10-28 12:46:35.243890: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-28 12:46:35.255506: 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 00:00:1761651995.268949 1764179 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761651995.273205 1764179 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761651995.283591 1764179 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761651995.283619 1764179 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761651995.283622 1764179 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761651995.283625 1764179 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-28 12:46:35.286923: 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 00:00:1761651997.675694 1764179 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13749 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 409,231 (1.56 MB)
 Trainable params: 409,231 (1.56 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/124
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761652000.222453 1764301 service.cc:152] XLA service 0x7a1700012560 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761652000.222551 1764301 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-28 12:46:40.281348: 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 00:00:1761652000.577813 1764301 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761652004.114521 1764301 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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
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Epoch 2/124

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Epoch 3/124

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Epoch 4/124

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Epoch 5/124

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Epoch 6/124

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[1m115/145[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1463 - loss: 2.6768
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Epoch 7/124

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Epoch 8/124

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Epoch 9/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1721 - loss: 2.5577 
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[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1764 - loss: 2.5484
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1770 - loss: 2.5467
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.1774 - loss: 2.5454 - val_accuracy: 0.2736 - val_loss: 2.2966
Epoch 10/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1815 - loss: 2.5413 
[1m 53/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1859 - loss: 2.5288
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1877 - loss: 2.5214
[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1888 - loss: 2.5163
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1894 - loss: 2.5123
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.1894 - loss: 2.5105 - val_accuracy: 0.2861 - val_loss: 2.2622
Epoch 11/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.2109 - loss: 2.5198
[1m 23/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1828 - loss: 2.5218 
[1m 47/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1823 - loss: 2.5116
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[1m 97/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1863 - loss: 2.4981
[1m120/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1879 - loss: 2.4926
[1m142/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.1890 - loss: 2.4889
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.1892 - loss: 2.4883 - val_accuracy: 0.2946 - val_loss: 2.2536
Epoch 12/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2082 - loss: 2.4245 
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[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2019 - loss: 2.4309
[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2016 - loss: 2.4312
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2014 - loss: 2.4321
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Epoch 13/124

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Epoch 14/124

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[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.2128 - loss: 2.3953
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Epoch 15/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.1797 - loss: 2.4056
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2084 - loss: 2.3919 
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[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2151 - loss: 2.3816
[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2165 - loss: 2.3770
[1m128/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2172 - loss: 2.3743
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Epoch 16/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2229 - loss: 2.3555 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2217 - loss: 2.3566
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[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2226 - loss: 2.3536
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2232 - loss: 2.3519
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2236 - loss: 2.3507 - val_accuracy: 0.3401 - val_loss: 2.1872
Epoch 17/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2372 - loss: 2.3196 
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[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2333 - loss: 2.3218
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2331 - loss: 2.3209
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2331 - loss: 2.3204 - val_accuracy: 0.3504 - val_loss: 2.1658
Epoch 18/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2578 - loss: 2.2831
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2383 - loss: 2.3208 
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[1m 97/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2387 - loss: 2.3131
[1m121/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2397 - loss: 2.3094
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2401 - loss: 2.3077 - val_accuracy: 0.3419 - val_loss: 2.1792
Epoch 19/124

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[1m 24/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2097 - loss: 2.3402 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2236 - loss: 2.3229
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[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2342 - loss: 2.3041
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2371 - loss: 2.2984
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.2389 - loss: 2.2950
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Epoch 20/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2188 - loss: 2.2488
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2524 - loss: 2.2442 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2540 - loss: 2.2486
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[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2538 - loss: 2.2532
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Epoch 21/124

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[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2653 - loss: 2.2389 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2596 - loss: 2.2456
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2576 - loss: 2.2459
[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2565 - loss: 2.2456
[1m127/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2559 - loss: 2.2452
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2556 - loss: 2.2450 - val_accuracy: 0.3568 - val_loss: 2.1334
Epoch 22/124

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[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2549 - loss: 2.2535 
[1m 48/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2577 - loss: 2.2437
[1m 72/145[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2591 - loss: 2.2406
[1m 97/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2598 - loss: 2.2389
[1m122/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2603 - loss: 2.2372
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.2604 - loss: 2.2361
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2605 - loss: 2.2361 - val_accuracy: 0.3578 - val_loss: 2.1222
Epoch 23/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2578 - loss: 2.1476
[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2515 - loss: 2.2239 
[1m 48/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2542 - loss: 2.2180
[1m 72/145[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2554 - loss: 2.2162
[1m 96/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2566 - loss: 2.2143
[1m120/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2570 - loss: 2.2142
[1m143/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.2575 - loss: 2.2144
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2576 - loss: 2.2144 - val_accuracy: 0.3589 - val_loss: 2.1295
Epoch 24/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.2969 - loss: 2.1280
[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2629 - loss: 2.2166 
[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2644 - loss: 2.2165
[1m 70/145[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2646 - loss: 2.2146
[1m 93/145[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2645 - loss: 2.2112
[1m118/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2648 - loss: 2.2088
[1m143/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.2653 - loss: 2.2071
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2653 - loss: 2.2069 - val_accuracy: 0.3576 - val_loss: 2.1393
Epoch 25/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.2969 - loss: 2.1520
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2858 - loss: 2.1730 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2811 - loss: 2.1835
[1m 75/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2792 - loss: 2.1864
[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2774 - loss: 2.1881
[1m123/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2767 - loss: 2.1879
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2763 - loss: 2.1874 - val_accuracy: 0.3581 - val_loss: 2.1202
Epoch 26/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2969 - loss: 2.1385
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2824 - loss: 2.1441 
[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2800 - loss: 2.1556
[1m 72/145[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2803 - loss: 2.1602
[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2807 - loss: 2.1625
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2813 - loss: 2.1637
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2818 - loss: 2.1641 - val_accuracy: 0.3560 - val_loss: 2.1264
Epoch 27/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.3203 - loss: 2.0380
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2995 - loss: 2.1412 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2955 - loss: 2.1474
[1m 73/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2924 - loss: 2.1497
[1m 97/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2909 - loss: 2.1514
[1m122/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2895 - loss: 2.1530
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2883 - loss: 2.1539 - val_accuracy: 0.3639 - val_loss: 2.1048
Epoch 28/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2707 - loss: 2.1512 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2770 - loss: 2.1491
[1m 74/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2789 - loss: 2.1504
[1m 97/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2799 - loss: 2.1511
[1m123/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2806 - loss: 2.1505
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2811 - loss: 2.1496 - val_accuracy: 0.3679 - val_loss: 2.0933
Epoch 29/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2825 - loss: 2.1657 
[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2841 - loss: 2.1583
[1m 74/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2851 - loss: 2.1535
[1m 97/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2863 - loss: 2.1496
[1m121/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2867 - loss: 2.1475
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.2871 - loss: 2.1458
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2872 - loss: 2.1457 - val_accuracy: 0.3669 - val_loss: 2.0768
Epoch 30/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2891 - loss: 2.0884
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2946 - loss: 2.1267 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2954 - loss: 2.1213
[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2948 - loss: 2.1220
[1m105/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2941 - loss: 2.1233
[1m129/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2937 - loss: 2.1239
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2935 - loss: 2.1238 - val_accuracy: 0.3699 - val_loss: 2.0724
Epoch 31/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2578 - loss: 2.3138
[1m 24/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2748 - loss: 2.1598 
[1m 48/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2810 - loss: 2.1374
[1m 73/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2844 - loss: 2.1286
[1m 96/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2859 - loss: 2.1253
[1m119/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2865 - loss: 2.1238
[1m143/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.2871 - loss: 2.1217
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2872 - loss: 2.1214 - val_accuracy: 0.3697 - val_loss: 2.0755
Epoch 32/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2734 - loss: 2.1719
[1m 28/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2874 - loss: 2.1110 
[1m 54/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2886 - loss: 2.1117
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2899 - loss: 2.1094
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2907 - loss: 2.1083
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2917 - loss: 2.1068
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2924 - loss: 2.1056 - val_accuracy: 0.3772 - val_loss: 2.0715
Epoch 33/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2188 - loss: 2.1375
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2836 - loss: 2.1164 
[1m 48/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2889 - loss: 2.1083
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[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2926 - loss: 2.1008
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2933 - loss: 2.0989
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Epoch 34/124

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[1m 47/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2902 - loss: 2.1034
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[1m 93/145[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2963 - loss: 2.0933
[1m118/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2981 - loss: 2.0890
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Epoch 35/124

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[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3004 - loss: 2.0836
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[1m144/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.3007 - loss: 2.0797
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Epoch 36/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 29ms/step - accuracy: 0.3516 - loss: 2.0633
[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3218 - loss: 2.0592 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3153 - loss: 2.0634
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3127 - loss: 2.0618
[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3112 - loss: 2.0611
[1m127/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3101 - loss: 2.0605
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3095 - loss: 2.0605 - val_accuracy: 0.3754 - val_loss: 2.0564
Epoch 37/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3340 - loss: 2.0374 
[1m 48/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3281 - loss: 2.0436
[1m 72/145[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3250 - loss: 2.0475
[1m 96/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3232 - loss: 2.0486
[1m120/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3220 - loss: 2.0483
[1m144/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.3213 - loss: 2.0481
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3212 - loss: 2.0481 - val_accuracy: 0.3826 - val_loss: 2.0416
Epoch 38/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.3281 - loss: 2.0686
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3248 - loss: 2.0291 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3203 - loss: 2.0297
[1m 75/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3189 - loss: 2.0325
[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3175 - loss: 2.0357
[1m123/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3169 - loss: 2.0370
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3168 - loss: 2.0377 - val_accuracy: 0.3871 - val_loss: 2.0234
Epoch 39/124

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[1m 24/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3145 - loss: 2.0787 
[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3178 - loss: 2.0526
[1m 73/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3183 - loss: 2.0443
[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3177 - loss: 2.0413
[1m122/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3170 - loss: 2.0403
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Epoch 40/124

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Epoch 41/124

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Epoch 42/124

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Epoch 43/124

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[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3364 - loss: 1.9891
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Epoch 44/124

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[1m 24/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3254 - loss: 2.0042 
[1m 48/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3261 - loss: 1.9975
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[1m 94/145[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3266 - loss: 1.9933
[1m119/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3272 - loss: 1.9913
[1m141/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.3276 - loss: 1.9898
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Epoch 45/124

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[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3345 - loss: 1.9794 
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[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3313 - loss: 1.9735
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Epoch 46/124

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[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3314 - loss: 1.9814 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3331 - loss: 1.9775
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[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3347 - loss: 1.9718
[1m126/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3346 - loss: 1.9710
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3344 - loss: 1.9709 - val_accuracy: 0.3923 - val_loss: 2.0100
Epoch 47/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.3125 - loss: 2.0070
[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3347 - loss: 1.9653 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3378 - loss: 1.9647
[1m 75/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3401 - loss: 1.9624
[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3411 - loss: 1.9611
[1m123/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3412 - loss: 1.9603
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3411 - loss: 1.9601 - val_accuracy: 0.3965 - val_loss: 1.9753
Epoch 48/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 25ms/step - accuracy: 0.3906 - loss: 1.9576
[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3523 - loss: 1.9432 
[1m 47/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3474 - loss: 1.9480
[1m 71/145[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3470 - loss: 1.9464
[1m 96/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3468 - loss: 1.9460
[1m120/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3468 - loss: 1.9470
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3468 - loss: 1.9477
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3468 - loss: 1.9477 - val_accuracy: 0.3909 - val_loss: 2.0216
Epoch 49/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.4453 - loss: 1.6967
[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3436 - loss: 1.8909 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3423 - loss: 1.9087
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3434 - loss: 1.9147
[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3441 - loss: 1.9190
[1m127/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3446 - loss: 1.9221
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3448 - loss: 1.9238 - val_accuracy: 0.4086 - val_loss: 2.0094
Epoch 50/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 27ms/step - accuracy: 0.4219 - loss: 1.6923
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3442 - loss: 1.9071 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3408 - loss: 1.9222
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3403 - loss: 1.9248
[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3406 - loss: 1.9259
[1m123/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3411 - loss: 1.9270
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3416 - loss: 1.9278 - val_accuracy: 0.4058 - val_loss: 2.0006
Epoch 51/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.3672 - loss: 1.8537
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3450 - loss: 1.9152 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3454 - loss: 1.9196
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3456 - loss: 1.9232
[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3463 - loss: 1.9245
[1m127/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3467 - loss: 1.9254
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3470 - loss: 1.9254 - val_accuracy: 0.4094 - val_loss: 1.9982
Epoch 52/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 25ms/step - accuracy: 0.3750 - loss: 1.9778
[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3606 - loss: 1.9459 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3575 - loss: 1.9433
[1m 75/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3572 - loss: 1.9369
[1m 97/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3571 - loss: 1.9322
[1m123/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3572 - loss: 1.9282
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3569 - loss: 1.9263 - val_accuracy: 0.4002 - val_loss: 2.0214

[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:14[0m 876ms/step
[1m55/86[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 932us/step  
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step 
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 11ms/step
Saved model to disk.

Accuracy capturado en la ejecución 2: 31.31 [%]
F1-score capturado en la ejecución 2: 31.48 [%]

=== EJECUCIÓN 3 ===

--- TRAIN (ejecución 3) ---

--- TEST (ejecución 3) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 409,231 (1.56 MB)
 Trainable params: 409,231 (1.56 MB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 6, 250)
(18469, 6, 250)

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m10:10[0m 1s/step
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[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m55/86[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 931us/step
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[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 10ms/step

[1m  1/158[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step
[1m 59/158[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 866us/step
[1m118/158[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 857us/step
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 40.02 [%]
Global F1 score (validation) = 38.4 [%]
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[[0.0016318  0.00091165 0.00131245 ... 0.03168361 0.00203323 0.00055643]
 [0.00103274 0.00087463 0.00126226 ... 0.07989769 0.00176924 0.00046884]
 [0.00099063 0.00040362 0.00100555 ... 0.02105383 0.00132181 0.00035279]
 ...
 [0.15020761 0.04222606 0.21805705 ... 0.00056988 0.225904   0.10396183]
 [0.17539372 0.05410137 0.21622683 ... 0.00056903 0.22686002 0.07712063]
 [0.169909   0.0432688  0.2147669  ... 0.00057157 0.2518243  0.07171492]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 42.93 [%]
Global accuracy score (test) = 32.74 [%]
Global F1 score (train) = 42.54 [%]
Global F1 score (test) = 32.52 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.28      0.33      0.30       184
 CAMINAR CON MÓVIL O LIBRO       0.39      0.31      0.35       184
       CAMINAR USUAL SPEED       0.11      0.09      0.10       184
            CAMINAR ZIGZAG       0.17      0.19      0.18       184
          DE PIE BARRIENDO       0.22      0.16      0.19       184
   DE PIE DOBLANDO TOALLAS       0.32      0.38      0.35       184
    DE PIE MOVIENDO LIBROS       0.33      0.22      0.26       184
          DE PIE USANDO PC       0.27      0.26      0.26       184
        FASE REPOSO CON K5       0.43      0.74      0.54       184
INCREMENTAL CICLOERGOMETRO       0.56      0.60      0.58       184
           SENTADO LEYENDO       0.55      0.38      0.45       184
         SENTADO USANDO PC       0.17      0.12      0.14       184
      SENTADO VIENDO LA TV       0.46      0.38      0.42       184
   SUBIR Y BAJAR ESCALERAS       0.12      0.20      0.15       184
                    TROTAR       0.64      0.60      0.62       161

                  accuracy                           0.33      2737
                 macro avg       0.33      0.33      0.33      2737
              weighted avg       0.33      0.33      0.32      2737

2025-10-28 12:47:25.696835: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-28 12:47:25.708048: 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 00:00:1761652045.721308 1770083 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761652045.725522 1770083 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761652045.735345 1770083 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761652045.735367 1770083 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761652045.735370 1770083 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761652045.735372 1770083 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-28 12:47:25.738651: 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 00:00:1761652048.095154 1770083 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13749 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 409,231 (1.56 MB)
 Trainable params: 409,231 (1.56 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/124
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761652050.675434 1770206 service.cc:152] XLA service 0x7fdc480131a0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761652050.675508 1770206 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-28 12:47:30.732324: 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 00:00:1761652051.032649 1770206 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761652054.650681 1770206 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13:52[0m 6s/step - accuracy: 0.0781 - loss: 3.0915
[1m 21/145[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.0763 - loss: 3.1970  
[1m 47/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.0759 - loss: 3.1991
[1m 71/145[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.0764 - loss: 3.1896
[1m 96/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.0775 - loss: 3.1777
[1m120/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.0789 - loss: 3.1677
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Epoch 2/124

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Epoch 3/124

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Epoch 4/124

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Epoch 5/124

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[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1414 - loss: 2.7201
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Epoch 6/124

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[1m120/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1498 - loss: 2.6734
[1m144/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.1506 - loss: 2.6710
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Epoch 7/124

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[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1608 - loss: 2.6263
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Epoch 8/124

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Epoch 9/124

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[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1777 - loss: 2.5302
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Epoch 10/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2056 - loss: 2.4792 
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[1m 79/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1960 - loss: 2.4914
[1m105/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1947 - loss: 2.4912
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[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.1940 - loss: 2.4905 - val_accuracy: 0.3032 - val_loss: 2.2422
Epoch 11/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2422 - loss: 2.3899
[1m 24/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2198 - loss: 2.4193 
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[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2074 - loss: 2.4431
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2059 - loss: 2.4455
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2052 - loss: 2.4464 - val_accuracy: 0.3143 - val_loss: 2.2080
Epoch 12/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1833 - loss: 2.4622 
[1m 53/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1882 - loss: 2.4526
[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1913 - loss: 2.4460
[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1934 - loss: 2.4422
[1m128/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1951 - loss: 2.4388
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.1960 - loss: 2.4372 - val_accuracy: 0.3252 - val_loss: 2.2062
Epoch 13/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2266 - loss: 2.3147
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2327 - loss: 2.3726 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2265 - loss: 2.3885
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2232 - loss: 2.3932
[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2215 - loss: 2.3940
[1m128/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2202 - loss: 2.3942
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2195 - loss: 2.3944 - val_accuracy: 0.3335 - val_loss: 2.1919
Epoch 14/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.1406 - loss: 2.4379
[1m 24/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2079 - loss: 2.3661 
[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2100 - loss: 2.3717
[1m 75/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2112 - loss: 2.3718
[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2122 - loss: 2.3720
[1m129/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2130 - loss: 2.3714
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2136 - loss: 2.3707 - val_accuracy: 0.3347 - val_loss: 2.1828
Epoch 15/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.1797 - loss: 2.4187
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2163 - loss: 2.3697 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2208 - loss: 2.3644
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2219 - loss: 2.3623
[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2222 - loss: 2.3614
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2227 - loss: 2.3610
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Epoch 16/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2266 - loss: 2.3618
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2208 - loss: 2.3396 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2231 - loss: 2.3363
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2243 - loss: 2.3345
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2254 - loss: 2.3333
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2258 - loss: 2.3333
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Epoch 17/124

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[1m 24/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2250 - loss: 2.3293 
[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2279 - loss: 2.3224
[1m 74/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2286 - loss: 2.3212
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2301 - loss: 2.3192
[1m126/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2314 - loss: 2.3173
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2322 - loss: 2.3164 - val_accuracy: 0.3439 - val_loss: 2.1573
Epoch 18/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2109 - loss: 2.2892
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2502 - loss: 2.2884 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2493 - loss: 2.2881
[1m 75/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2490 - loss: 2.2883
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2482 - loss: 2.2893
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2474 - loss: 2.2913
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2468 - loss: 2.2927 - val_accuracy: 0.3470 - val_loss: 2.1574
Epoch 19/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.1797 - loss: 2.4303
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2398 - loss: 2.2926 
[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2403 - loss: 2.2847
[1m 75/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2413 - loss: 2.2822
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2426 - loss: 2.2800
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2433 - loss: 2.2792
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2437 - loss: 2.2786 - val_accuracy: 0.3484 - val_loss: 2.1605
Epoch 20/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.2578 - loss: 2.2974
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2524 - loss: 2.2787 
[1m 54/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2514 - loss: 2.2741
[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2510 - loss: 2.2731
[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2509 - loss: 2.2721
[1m127/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2512 - loss: 2.2701
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2513 - loss: 2.2689 - val_accuracy: 0.3534 - val_loss: 2.1392
Epoch 21/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2031 - loss: 2.2109
[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2470 - loss: 2.2472 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2464 - loss: 2.2559
[1m 75/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2469 - loss: 2.2565
[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2477 - loss: 2.2561
[1m127/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2486 - loss: 2.2554
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2492 - loss: 2.2543 - val_accuracy: 0.3514 - val_loss: 2.1316
Epoch 22/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2656 - loss: 2.1519
[1m 28/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2558 - loss: 2.2321 
[1m 53/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2564 - loss: 2.2346
[1m 80/145[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2567 - loss: 2.2323
[1m107/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2566 - loss: 2.2308
[1m133/145[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.2567 - loss: 2.2306
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2569 - loss: 2.2306 - val_accuracy: 0.3663 - val_loss: 2.1295
Epoch 23/124

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[1m 28/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2691 - loss: 2.2244 
[1m 55/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2695 - loss: 2.2194
[1m 81/145[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2695 - loss: 2.2169
[1m107/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2692 - loss: 2.2161
[1m132/145[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.2687 - loss: 2.2155
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2686 - loss: 2.2151 - val_accuracy: 0.3520 - val_loss: 2.1214
Epoch 24/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2422 - loss: 2.2918
[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2643 - loss: 2.2139 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2658 - loss: 2.2117
[1m 73/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2682 - loss: 2.2056
[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2691 - loss: 2.2035
[1m123/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2693 - loss: 2.2026
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.2694 - loss: 2.2018
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2694 - loss: 2.2018 - val_accuracy: 0.3611 - val_loss: 2.1122
Epoch 25/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2031 - loss: 2.2642
[1m 24/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2658 - loss: 2.2110 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2683 - loss: 2.2014
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2684 - loss: 2.1972
[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2678 - loss: 2.1951
[1m128/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2682 - loss: 2.1929
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2686 - loss: 2.1916 - val_accuracy: 0.3603 - val_loss: 2.1022
Epoch 26/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 25ms/step - accuracy: 0.3047 - loss: 2.1556
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2742 - loss: 2.1831 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2758 - loss: 2.1760
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2765 - loss: 2.1745
[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2766 - loss: 2.1738
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2767 - loss: 2.1736
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2766 - loss: 2.1732 - val_accuracy: 0.3520 - val_loss: 2.1113
Epoch 27/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.2656 - loss: 2.2082
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2891 - loss: 2.1468 
[1m 48/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2835 - loss: 2.1602
[1m 74/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2811 - loss: 2.1637
[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2795 - loss: 2.1648
[1m119/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2788 - loss: 2.1655
[1m144/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.2783 - loss: 2.1657
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2782 - loss: 2.1657 - val_accuracy: 0.3639 - val_loss: 2.0984
Epoch 28/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.2969 - loss: 2.0856
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2853 - loss: 2.1183 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2869 - loss: 2.1287
[1m 75/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2871 - loss: 2.1325
[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2867 - loss: 2.1354
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2868 - loss: 2.1382
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2867 - loss: 2.1399 - val_accuracy: 0.3583 - val_loss: 2.1034
Epoch 29/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.2734 - loss: 2.1315
[1m 23/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3057 - loss: 2.0956 
[1m 47/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3019 - loss: 2.1110
[1m 74/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2991 - loss: 2.1184
[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2971 - loss: 2.1231
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2955 - loss: 2.1261
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2945 - loss: 2.1283 - val_accuracy: 0.3720 - val_loss: 2.0941
Epoch 30/124

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Epoch 31/124

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[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2881 - loss: 2.1165
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Epoch 32/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2927 - loss: 2.1182 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2944 - loss: 2.1101
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[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2944 - loss: 2.1054
[1m126/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2942 - loss: 2.1051
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Epoch 33/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2969 - loss: 2.0672
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[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2934 - loss: 2.0869
[1m 74/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2930 - loss: 2.0910
[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2931 - loss: 2.0923
[1m128/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2933 - loss: 2.0920
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2937 - loss: 2.0915 - val_accuracy: 0.3703 - val_loss: 2.0744
Epoch 34/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2266 - loss: 2.0864
[1m 24/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3037 - loss: 2.0303 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3021 - loss: 2.0480
[1m 75/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3013 - loss: 2.0575
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3001 - loss: 2.0654
[1m121/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2993 - loss: 2.0693
[1m142/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.2988 - loss: 2.0717
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2988 - loss: 2.0721 - val_accuracy: 0.3693 - val_loss: 2.0507
Epoch 35/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.2734 - loss: 2.1287
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3228 - loss: 2.0453 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3183 - loss: 2.0545
[1m 75/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3154 - loss: 2.0593
[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3135 - loss: 2.0626
[1m123/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3119 - loss: 2.0650
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3108 - loss: 2.0663 - val_accuracy: 0.3754 - val_loss: 2.0526
Epoch 36/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3153 - loss: 2.0560 
[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3106 - loss: 2.0611
[1m 74/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3095 - loss: 2.0607
[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3086 - loss: 2.0602
[1m123/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3088 - loss: 2.0584
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3090 - loss: 2.0576 - val_accuracy: 0.3693 - val_loss: 2.0542
Epoch 37/124

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[1m 53/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3137 - loss: 2.0625
[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3118 - loss: 2.0635
[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3115 - loss: 2.0611
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Epoch 38/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2915 - loss: 2.0487 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2996 - loss: 2.0484
[1m 75/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3022 - loss: 2.0493
[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3042 - loss: 2.0487
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Epoch 39/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3036 - loss: 2.0214 
[1m 53/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3073 - loss: 2.0165
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3092 - loss: 2.0150
[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3111 - loss: 2.0150
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Epoch 40/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3030 - loss: 2.0519 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3124 - loss: 2.0384
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3151 - loss: 2.0352
[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3172 - loss: 2.0322
[1m128/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3184 - loss: 2.0297
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3189 - loss: 2.0280 - val_accuracy: 0.3774 - val_loss: 2.0127
Epoch 41/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3375 - loss: 2.0344 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3329 - loss: 2.0259
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3317 - loss: 2.0176
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3305 - loss: 2.0157
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3294 - loss: 2.0145
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3287 - loss: 2.0134 - val_accuracy: 0.3812 - val_loss: 2.0368
Epoch 42/124

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[1m 28/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3194 - loss: 1.9995 
[1m 54/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3198 - loss: 1.9992
[1m 79/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3197 - loss: 2.0010
[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3193 - loss: 2.0034
[1m128/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3189 - loss: 2.0052
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3190 - loss: 2.0058 - val_accuracy: 0.3784 - val_loss: 2.0227
Epoch 43/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3272 - loss: 1.9592 
[1m 47/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3290 - loss: 1.9645
[1m 71/145[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3305 - loss: 1.9648
[1m 96/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3307 - loss: 1.9672
[1m122/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3303 - loss: 1.9699
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3296 - loss: 1.9725 - val_accuracy: 0.3945 - val_loss: 2.0095
Epoch 44/124

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[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3309 - loss: 1.9760
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Epoch 45/124

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[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3316 - loss: 1.9786
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Epoch 46/124

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[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3321 - loss: 1.9720
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Epoch 47/124

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[1m 23/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3510 - loss: 1.9373 
[1m 47/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3477 - loss: 1.9414
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[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3450 - loss: 1.9451
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3439 - loss: 1.9469
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3432 - loss: 1.9480 - val_accuracy: 0.3834 - val_loss: 1.9954
Epoch 48/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3565 - loss: 1.9147 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3545 - loss: 1.9203
[1m 75/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3516 - loss: 1.9256
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3483 - loss: 1.9306
[1m127/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3463 - loss: 1.9336
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3454 - loss: 1.9350 - val_accuracy: 0.3907 - val_loss: 1.9845
Epoch 49/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 26ms/step - accuracy: 0.3672 - loss: 2.0537
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3524 - loss: 1.9409 
[1m 54/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3481 - loss: 1.9404
[1m 80/145[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3464 - loss: 1.9383
[1m106/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3448 - loss: 1.9389
[1m131/145[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.3439 - loss: 1.9402
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3438 - loss: 1.9404 - val_accuracy: 0.4044 - val_loss: 1.9977
Epoch 50/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 26ms/step - accuracy: 0.3984 - loss: 1.9171
[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3627 - loss: 1.9087 
[1m 48/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3501 - loss: 1.9259
[1m 71/145[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3471 - loss: 1.9305
[1m 96/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3463 - loss: 1.9306
[1m121/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3459 - loss: 1.9307
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3458 - loss: 1.9308
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3458 - loss: 1.9308 - val_accuracy: 0.3988 - val_loss: 1.9829
Epoch 51/124

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Epoch 52/124

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Epoch 53/124

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Epoch 54/124

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[1m 96/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3619 - loss: 1.8910
[1m121/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3605 - loss: 1.8935
[1m144/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.3596 - loss: 1.8951
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Epoch 55/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3568 - loss: 1.9059 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3574 - loss: 1.8990
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[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3589 - loss: 1.8939
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Epoch 56/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3820 - loss: 1.8690 
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Epoch 57/124

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[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3649 - loss: 1.8853
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[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3632 - loss: 1.8860 - val_accuracy: 0.4030 - val_loss: 1.9886
Epoch 58/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.3828 - loss: 1.8641
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3681 - loss: 1.8971 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3646 - loss: 1.8957
[1m 75/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3639 - loss: 1.8913
[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3642 - loss: 1.8870
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3650 - loss: 1.8830
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3651 - loss: 1.8811 - val_accuracy: 0.4030 - val_loss: 1.9917
Epoch 59/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.3438 - loss: 1.9859
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3682 - loss: 1.8510 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3672 - loss: 1.8534
[1m 75/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3667 - loss: 1.8552
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3662 - loss: 1.8587
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3663 - loss: 1.8608
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3664 - loss: 1.8615 - val_accuracy: 0.4020 - val_loss: 1.9905

[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:13[0m 864ms/step
[1m52/86[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 985us/step  
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step 
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 11ms/step
Saved model to disk.

Accuracy capturado en la ejecución 3: 32.74 [%]
F1-score capturado en la ejecución 3: 32.52 [%]

=== EJECUCIÓN 4 ===

--- TRAIN (ejecución 4) ---

--- TEST (ejecución 4) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 409,231 (1.56 MB)
 Trainable params: 409,231 (1.56 MB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 6, 250)
(18469, 6, 250)

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[1m 56/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 923us/step
[1m121/578[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 842us/step
[1m180/578[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 845us/step
[1m245/578[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 825us/step
[1m303/578[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 834us/step
[1m366/578[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 829us/step
[1m420/578[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 843us/step
[1m476/578[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 849us/step
[1m542/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 838us/step
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 2ms/step

[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 17ms/step
[1m59/86[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 866us/step
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step  
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 9ms/step

[1m  1/158[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 17ms/step
[1m 59/158[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 865us/step
[1m118/158[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 858us/step
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 40.2 [%]
Global F1 score (validation) = 38.23 [%]
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[[8.99811916e-04 2.89011776e-04 5.38895139e-04 ... 1.60452202e-02
  1.18814153e-03 3.54195130e-04]
 [8.36291059e-04 4.24861821e-04 6.54923904e-04 ... 3.32034007e-02
  1.30442530e-03 3.18554783e-04]
 [4.28620784e-04 1.21400575e-04 3.46164365e-04 ... 1.54277012e-02
  5.82580746e-04 2.87486560e-04]
 ...
 [1.73776075e-01 4.74257767e-02 2.03429699e-01 ... 6.56304008e-04
  2.20796049e-01 8.72743502e-02]
 [1.99823096e-01 7.65298828e-02 1.85071155e-01 ... 1.31642900e-03
  1.95370704e-01 6.99573606e-02]
 [1.99306324e-01 5.21784350e-02 1.87608853e-01 ... 7.78534915e-04
  2.43198127e-01 6.29591122e-02]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 44.99 [%]
Global accuracy score (test) = 35.48 [%]
Global F1 score (train) = 44.73 [%]
Global F1 score (test) = 34.67 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.35      0.46      0.40       184
 CAMINAR CON MÓVIL O LIBRO       0.36      0.32      0.34       184
       CAMINAR USUAL SPEED       0.18      0.08      0.11       184
            CAMINAR ZIGZAG       0.24      0.39      0.29       184
          DE PIE BARRIENDO       0.28      0.22      0.24       184
   DE PIE DOBLANDO TOALLAS       0.34      0.33      0.33       184
    DE PIE MOVIENDO LIBROS       0.44      0.27      0.34       184
          DE PIE USANDO PC       0.25      0.24      0.25       184
        FASE REPOSO CON K5       0.42      0.74      0.54       184
INCREMENTAL CICLOERGOMETRO       0.56      0.67      0.61       184
           SENTADO LEYENDO       0.43      0.30      0.36       184
         SENTADO USANDO PC       0.11      0.08      0.09       184
      SENTADO VIENDO LA TV       0.34      0.39      0.36       184
   SUBIR Y BAJAR ESCALERAS       0.25      0.29      0.27       184
                    TROTAR       0.82      0.58      0.68       161

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

2025-10-28 12:48:19.174506: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-28 12:48:19.185713: 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 00:00:1761652099.198958 1776627 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761652099.203182 1776627 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761652099.213126 1776627 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761652099.213148 1776627 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761652099.213151 1776627 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761652099.213153 1776627 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-28 12:48:19.216405: 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 00:00:1761652101.566327 1776627 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13749 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 409,231 (1.56 MB)
 Trainable params: 409,231 (1.56 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/124
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761652104.110625 1776757 service.cc:152] XLA service 0x74bf40005c00 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761652104.110671 1776757 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-28 12:48:24.166329: 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 00:00:1761652104.465112 1776757 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761652108.123235 1776757 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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
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Epoch 2/124

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Epoch 3/124

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Epoch 4/124

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Epoch 5/124

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Epoch 6/124

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Epoch 7/124

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Epoch 8/124

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[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1688 - loss: 2.5961
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Epoch 9/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1788 - loss: 2.5486 
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[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1777 - loss: 2.5388
[1m123/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1781 - loss: 2.5377
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.1785 - loss: 2.5374 - val_accuracy: 0.3051 - val_loss: 2.2729
Epoch 10/124

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[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1862 - loss: 2.4695 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1886 - loss: 2.4765
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[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1873 - loss: 2.4880
[1m128/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1873 - loss: 2.4903
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.1874 - loss: 2.4906 - val_accuracy: 0.3171 - val_loss: 2.2544
Epoch 11/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.1719 - loss: 2.6680
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1897 - loss: 2.4891 
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[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1864 - loss: 2.4876
[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1862 - loss: 2.4862
[1m129/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1866 - loss: 2.4843
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.1871 - loss: 2.4830 - val_accuracy: 0.3184 - val_loss: 2.2434
Epoch 12/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2050 - loss: 2.4420 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2037 - loss: 2.4468
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[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2033 - loss: 2.4429
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2031 - loss: 2.4417
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Epoch 13/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2266 - loss: 2.3921
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1974 - loss: 2.4120 
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[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2031 - loss: 2.4145
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Epoch 14/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2390 - loss: 2.3555 
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[1m 79/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2261 - loss: 2.3799
[1m104/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2239 - loss: 2.3828
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[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2222 - loss: 2.3847 - val_accuracy: 0.3234 - val_loss: 2.2096
Epoch 15/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2240 - loss: 2.3700 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2213 - loss: 2.3720
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2210 - loss: 2.3698
[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2208 - loss: 2.3686
[1m128/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2208 - loss: 2.3672
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2208 - loss: 2.3665 - val_accuracy: 0.3355 - val_loss: 2.1922
Epoch 16/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2656 - loss: 2.3063
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2161 - loss: 2.3669 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2165 - loss: 2.3634
[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2187 - loss: 2.3595
[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2209 - loss: 2.3560
[1m128/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2218 - loss: 2.3543
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2226 - loss: 2.3528 - val_accuracy: 0.3401 - val_loss: 2.1900
Epoch 17/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.2656 - loss: 2.2095
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2348 - loss: 2.3187 
[1m 54/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2281 - loss: 2.3313
[1m 79/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2280 - loss: 2.3319
[1m104/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2290 - loss: 2.3305
[1m128/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2300 - loss: 2.3288
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2306 - loss: 2.3274 - val_accuracy: 0.3518 - val_loss: 2.1716
Epoch 18/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.3125 - loss: 2.0525
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2394 - loss: 2.3149 
[1m 53/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2410 - loss: 2.3094
[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2417 - loss: 2.3062
[1m104/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2415 - loss: 2.3044
[1m131/145[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.2412 - loss: 2.3028
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2411 - loss: 2.3025 - val_accuracy: 0.3441 - val_loss: 2.1740
Epoch 19/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2266 - loss: 2.3640
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2494 - loss: 2.2920 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2543 - loss: 2.2855
[1m 75/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2542 - loss: 2.2838
[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2532 - loss: 2.2829
[1m126/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2525 - loss: 2.2819
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2521 - loss: 2.2811 - val_accuracy: 0.3450 - val_loss: 2.1536
Epoch 20/124

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[1m 24/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2516 - loss: 2.2589 
[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2530 - loss: 2.2613
[1m 74/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2539 - loss: 2.2639
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2541 - loss: 2.2639
[1m126/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2539 - loss: 2.2630
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2539 - loss: 2.2628 - val_accuracy: 0.3536 - val_loss: 2.1440
Epoch 21/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.2578 - loss: 2.1608
[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2597 - loss: 2.2589 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2540 - loss: 2.2677
[1m 75/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2527 - loss: 2.2675
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2522 - loss: 2.2661
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2524 - loss: 2.2635
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2528 - loss: 2.2615 - val_accuracy: 0.3572 - val_loss: 2.1368
Epoch 22/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2266 - loss: 2.3075
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2618 - loss: 2.2286 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2589 - loss: 2.2308
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2590 - loss: 2.2313
[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2589 - loss: 2.2315
[1m128/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2589 - loss: 2.2310
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2588 - loss: 2.2310 - val_accuracy: 0.3653 - val_loss: 2.1328
Epoch 23/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.2422 - loss: 2.2711
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2690 - loss: 2.2045 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2729 - loss: 2.2009
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2731 - loss: 2.2006
[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2720 - loss: 2.2032
[1m127/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2710 - loss: 2.2050
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2703 - loss: 2.2062 - val_accuracy: 0.3647 - val_loss: 2.1345
Epoch 24/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2500 - loss: 2.2121
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2565 - loss: 2.2104 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2568 - loss: 2.2121
[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2576 - loss: 2.2104
[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2594 - loss: 2.2082
[1m126/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2607 - loss: 2.2069
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2614 - loss: 2.2066 - val_accuracy: 0.3649 - val_loss: 2.1152
Epoch 25/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.3047 - loss: 2.0963
[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2689 - loss: 2.2024 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2665 - loss: 2.2050
[1m 74/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2671 - loss: 2.2025
[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2673 - loss: 2.2018
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2673 - loss: 2.2009
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2675 - loss: 2.2000 - val_accuracy: 0.3679 - val_loss: 2.1278
Epoch 26/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.2578 - loss: 2.2639
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2817 - loss: 2.1813 
[1m 53/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2829 - loss: 2.1718
[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2824 - loss: 2.1688
[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2817 - loss: 2.1693
[1m127/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2813 - loss: 2.1698
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2812 - loss: 2.1700 - val_accuracy: 0.3589 - val_loss: 2.1092
Epoch 27/124

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[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2906 - loss: 2.1309 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2873 - loss: 2.1446
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2857 - loss: 2.1509
[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2848 - loss: 2.1539
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2839 - loss: 2.1561
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2834 - loss: 2.1574 - val_accuracy: 0.3649 - val_loss: 2.1080
Epoch 28/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.3672 - loss: 2.0932
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2987 - loss: 2.1433 
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[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2895 - loss: 2.1496
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2889 - loss: 2.1496
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2882 - loss: 2.1501 - val_accuracy: 0.3701 - val_loss: 2.0810
Epoch 29/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.3125 - loss: 2.0172
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2939 - loss: 2.1042 
[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2902 - loss: 2.1173
[1m 75/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2877 - loss: 2.1264
[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2871 - loss: 2.1313
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2876 - loss: 2.1332
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2880 - loss: 2.1334 - val_accuracy: 0.3647 - val_loss: 2.0850
Epoch 30/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.3203 - loss: 1.9489
[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3144 - loss: 2.1034 
[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3073 - loss: 2.1140
[1m 71/145[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3035 - loss: 2.1201
[1m 97/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3007 - loss: 2.1227
[1m122/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2993 - loss: 2.1229
[1m144/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.2983 - loss: 2.1234
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2983 - loss: 2.1234 - val_accuracy: 0.3740 - val_loss: 2.0715
Epoch 31/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2656 - loss: 2.1975
[1m 28/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2830 - loss: 2.1591 
[1m 53/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2879 - loss: 2.1456
[1m 79/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2905 - loss: 2.1390
[1m105/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2923 - loss: 2.1352
[1m131/145[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.2933 - loss: 2.1323
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2936 - loss: 2.1306 - val_accuracy: 0.3679 - val_loss: 2.0630
Epoch 32/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.3047 - loss: 2.1898
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3069 - loss: 2.1014 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3049 - loss: 2.1012
[1m 74/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3039 - loss: 2.1009
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3033 - loss: 2.1016
[1m126/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3026 - loss: 2.1019
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3021 - loss: 2.1023 - val_accuracy: 0.3689 - val_loss: 2.0930
Epoch 33/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.2969 - loss: 2.1154
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2982 - loss: 2.0955 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2981 - loss: 2.0962
[1m 79/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2971 - loss: 2.0972
[1m104/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2969 - loss: 2.0980
[1m128/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2971 - loss: 2.0976
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2974 - loss: 2.0972 - val_accuracy: 0.3752 - val_loss: 2.0634
Epoch 34/124

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[1m 54/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3100 - loss: 2.0740
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[1m105/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3091 - loss: 2.0752
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Epoch 35/124

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[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2946 - loss: 2.0836
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Epoch 36/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3075 - loss: 2.0519 
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[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3003 - loss: 2.0618
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3005 - loss: 2.0629
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Epoch 37/124

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[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3135 - loss: 2.0564
[1m 72/145[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3142 - loss: 2.0570
[1m 97/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3144 - loss: 2.0568
[1m122/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3143 - loss: 2.0563
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3145 - loss: 2.0558 - val_accuracy: 0.3786 - val_loss: 2.0347
Epoch 38/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3043 - loss: 2.0869 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3092 - loss: 2.0724
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3098 - loss: 2.0681
[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3099 - loss: 2.0648
[1m126/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3102 - loss: 2.0619
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3107 - loss: 2.0599 - val_accuracy: 0.3738 - val_loss: 2.0298
Epoch 39/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3069 - loss: 2.0310 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3114 - loss: 2.0311
[1m 73/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3140 - loss: 2.0293
[1m 97/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3154 - loss: 2.0296
[1m123/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3154 - loss: 2.0304
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3154 - loss: 2.0306 - val_accuracy: 0.3885 - val_loss: 2.0277
Epoch 40/124

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[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3114 - loss: 2.0163 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3156 - loss: 2.0168
[1m 74/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3168 - loss: 2.0167
[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3172 - loss: 2.0180
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3176 - loss: 2.0188
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3178 - loss: 2.0197 - val_accuracy: 0.3927 - val_loss: 2.0192
Epoch 41/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3164 - loss: 2.0611 
[1m 53/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3162 - loss: 2.0538
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3163 - loss: 2.0497
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3163 - loss: 2.0466
[1m126/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3170 - loss: 2.0423
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3176 - loss: 2.0392 - val_accuracy: 0.3756 - val_loss: 2.0326
Epoch 42/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 25ms/step - accuracy: 0.3203 - loss: 2.0239
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3228 - loss: 1.9911 
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[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3197 - loss: 2.0033
[1m105/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3195 - loss: 2.0069
[1m131/145[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.3199 - loss: 2.0085
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3200 - loss: 2.0090 - val_accuracy: 0.4012 - val_loss: 1.9985
Epoch 43/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.3125 - loss: 1.9162
[1m 23/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3208 - loss: 1.9865 
[1m 46/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3229 - loss: 1.9912
[1m 70/145[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3243 - loss: 1.9913
[1m 96/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3250 - loss: 1.9906
[1m120/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3256 - loss: 1.9895
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3255 - loss: 1.9899 - val_accuracy: 0.3822 - val_loss: 2.0032
Epoch 44/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.2812 - loss: 2.1380
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3002 - loss: 2.0310 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3089 - loss: 2.0151
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3140 - loss: 2.0088
[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3175 - loss: 2.0037
[1m127/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3194 - loss: 2.0008
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3204 - loss: 1.9996 - val_accuracy: 0.3836 - val_loss: 2.0065
Epoch 45/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.3906 - loss: 1.8804
[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3499 - loss: 1.9362 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3434 - loss: 1.9511
[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3406 - loss: 1.9572
[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3397 - loss: 1.9602
[1m127/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3391 - loss: 1.9626
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3388 - loss: 1.9641 - val_accuracy: 0.3869 - val_loss: 2.0150
Epoch 46/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.3203 - loss: 2.0427
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3231 - loss: 1.9849 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3294 - loss: 1.9732
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3312 - loss: 1.9707
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3322 - loss: 1.9700
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3324 - loss: 1.9701
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3325 - loss: 1.9699 - val_accuracy: 0.3994 - val_loss: 2.0386
Epoch 47/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.4297 - loss: 1.8055
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3453 - loss: 1.9724 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3429 - loss: 1.9688
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3411 - loss: 1.9669
[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3400 - loss: 1.9648
[1m126/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3396 - loss: 1.9640
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3396 - loss: 1.9636 - val_accuracy: 0.3832 - val_loss: 2.0045

[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:13[0m 868ms/step
[1m59/86[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 866us/step  
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step 
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 11ms/step
Saved model to disk.

Accuracy capturado en la ejecución 4: 35.48 [%]
F1-score capturado en la ejecución 4: 34.67 [%]

=== EJECUCIÓN 5 ===

--- TRAIN (ejecución 5) ---

--- TEST (ejecución 5) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 409,231 (1.56 MB)
 Trainable params: 409,231 (1.56 MB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 6, 250)
(18469, 6, 250)

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m10:00[0m 1s/step
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[1m187/578[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 813us/step
[1m257/578[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 788us/step
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[1m384/578[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 790us/step
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[1m509/578[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 795us/step
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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 2ms/step

[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 17ms/step
[1m61/86[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 842us/step
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step  
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 10ms/step

[1m  1/158[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 17ms/step
[1m 60/158[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 851us/step
[1m121/158[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 841us/step
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 38.32 [%]
Global F1 score (validation) = 35.83 [%]
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[[0.00236975 0.00129946 0.00227644 ... 0.0364734  0.00366884 0.00076106]
 [0.00249966 0.0017744  0.00279314 ... 0.06605582 0.00438875 0.0008381 ]
 [0.00281807 0.00092974 0.00286273 ... 0.05794225 0.00434712 0.00129931]
 ...
 [0.17923002 0.04051614 0.20012347 ... 0.00073724 0.23739637 0.08310907]
 [0.16128775 0.13385968 0.08715788 ... 0.01017926 0.09234204 0.04393544]
 [0.16556498 0.03760295 0.20072794 ... 0.00063669 0.25141305 0.09300487]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 40.19 [%]
Global accuracy score (test) = 31.75 [%]
Global F1 score (train) = 39.09 [%]
Global F1 score (test) = 31.19 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.28      0.19      0.23       184
 CAMINAR CON MÓVIL O LIBRO       0.39      0.35      0.37       184
       CAMINAR USUAL SPEED       0.12      0.04      0.06       184
            CAMINAR ZIGZAG       0.08      0.06      0.07       184
          DE PIE BARRIENDO       0.21      0.21      0.21       184
   DE PIE DOBLANDO TOALLAS       0.39      0.40      0.39       184
    DE PIE MOVIENDO LIBROS       0.40      0.24      0.30       184
          DE PIE USANDO PC       0.24      0.24      0.24       184
        FASE REPOSO CON K5       0.41      0.74      0.53       184
INCREMENTAL CICLOERGOMETRO       0.55      0.61      0.58       184
           SENTADO LEYENDO       0.47      0.29      0.36       184
         SENTADO USANDO PC       0.12      0.12      0.12       184
      SENTADO VIENDO LA TV       0.36      0.35      0.35       184
   SUBIR Y BAJAR ESCALERAS       0.16      0.41      0.23       184
                    TROTAR       0.78      0.54      0.64       161

                  accuracy                           0.32      2737
                 macro avg       0.33      0.32      0.31      2737
              weighted avg       0.33      0.32      0.31      2737

2025-10-28 12:49:07.487344: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-28 12:49:07.498771: 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 00:00:1761652147.512103 1782055 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761652147.516351 1782055 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761652147.526345 1782055 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761652147.526370 1782055 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761652147.526374 1782055 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761652147.526377 1782055 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-28 12:49:07.529674: 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 00:00:1761652149.925646 1782055 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13749 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 409,231 (1.56 MB)
 Trainable params: 409,231 (1.56 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/124
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761652152.550013 1782186 service.cc:152] XLA service 0x7960000127f0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761652152.550078 1782186 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-28 12:49:12.606930: 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 00:00:1761652152.903767 1782186 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761652156.576620 1782186 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m14:06[0m 6s/step - accuracy: 0.1172 - loss: 3.0301
[1m 20/145[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.0733 - loss: 3.1893  
[1m 46/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.0753 - loss: 3.1793
[1m 72/145[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.0780 - loss: 3.1615
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[1m126/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.0822 - loss: 3.1320
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Epoch 2/124

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Epoch 3/124

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Epoch 4/124

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[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1365 - loss: 2.7642
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Epoch 5/124

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[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1525 - loss: 2.7074
[1m123/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1519 - loss: 2.7055
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.1515 - loss: 2.7043 - val_accuracy: 0.2150 - val_loss: 2.3805
Epoch 6/124

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[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1499 - loss: 2.6534
[1m122/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1505 - loss: 2.6529
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.1507 - loss: 2.6528 - val_accuracy: 0.2295 - val_loss: 2.3512
Epoch 7/124

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[1m 94/145[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1637 - loss: 2.6132
[1m118/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1637 - loss: 2.6128
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Epoch 8/124

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Epoch 9/124

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Epoch 10/124

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Epoch 11/124

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[1m 97/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1937 - loss: 2.4741
[1m123/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1930 - loss: 2.4752
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Epoch 12/124

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[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2008 - loss: 2.4443 
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[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2016 - loss: 2.4342
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2015 - loss: 2.4321
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2015 - loss: 2.4314 - val_accuracy: 0.3194 - val_loss: 2.2207
Epoch 13/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2224 - loss: 2.3970 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2176 - loss: 2.4053
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[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2153 - loss: 2.4079
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2151 - loss: 2.4069
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2150 - loss: 2.4065 - val_accuracy: 0.3325 - val_loss: 2.2058
Epoch 14/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2500 - loss: 2.4512
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2132 - loss: 2.4226 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2147 - loss: 2.4110
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[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2144 - loss: 2.4048
[1m122/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2145 - loss: 2.4034
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2149 - loss: 2.4014 - val_accuracy: 0.3381 - val_loss: 2.1950
Epoch 15/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.2266 - loss: 2.4071
[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2161 - loss: 2.3697 
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[1m 96/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2242 - loss: 2.3640
[1m118/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2246 - loss: 2.3629
[1m141/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.2251 - loss: 2.3617
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2252 - loss: 2.3614 - val_accuracy: 0.3425 - val_loss: 2.1756
Epoch 16/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2344 - loss: 2.3108
[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2230 - loss: 2.3581 
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[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2236 - loss: 2.3488
[1m123/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2245 - loss: 2.3469
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2255 - loss: 2.3456 - val_accuracy: 0.3587 - val_loss: 2.1503
Epoch 17/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2434 - loss: 2.3179 
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[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2427 - loss: 2.3094
[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2424 - loss: 2.3081
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Epoch 18/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2734 - loss: 2.2482
[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2468 - loss: 2.2791 
[1m 48/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2454 - loss: 2.2787
[1m 74/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2451 - loss: 2.2813
[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2448 - loss: 2.2831
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2445 - loss: 2.2844
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2445 - loss: 2.2848 - val_accuracy: 0.3619 - val_loss: 2.1367
Epoch 19/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2578 - loss: 2.2477
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2520 - loss: 2.2681 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2537 - loss: 2.2643
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2532 - loss: 2.2664
[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2528 - loss: 2.2680
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2526 - loss: 2.2689
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2522 - loss: 2.2699 - val_accuracy: 0.3627 - val_loss: 2.1276
Epoch 20/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.3125 - loss: 2.2687
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2585 - loss: 2.2731 
[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2587 - loss: 2.2687
[1m 75/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2582 - loss: 2.2648
[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2580 - loss: 2.2625
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2579 - loss: 2.2606
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2580 - loss: 2.2597 - val_accuracy: 0.3738 - val_loss: 2.1271
Epoch 21/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2188 - loss: 2.3094
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2478 - loss: 2.2680 
[1m 48/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2533 - loss: 2.2546
[1m 73/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2549 - loss: 2.2505
[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2563 - loss: 2.2491
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2572 - loss: 2.2486
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2578 - loss: 2.2481 - val_accuracy: 0.3643 - val_loss: 2.1224
Epoch 22/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.1953 - loss: 2.2771
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2585 - loss: 2.2458 
[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2616 - loss: 2.2341
[1m 74/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2624 - loss: 2.2324
[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2637 - loss: 2.2299
[1m121/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2639 - loss: 2.2289
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.2638 - loss: 2.2283
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2638 - loss: 2.2283 - val_accuracy: 0.3599 - val_loss: 2.1202
Epoch 23/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.2344 - loss: 2.2891
[1m 24/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2510 - loss: 2.2299 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2549 - loss: 2.2272
[1m 75/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2555 - loss: 2.2266
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2565 - loss: 2.2256
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2578 - loss: 2.2244
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2589 - loss: 2.2230 - val_accuracy: 0.3709 - val_loss: 2.0929
Epoch 24/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.3281 - loss: 2.0890
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2751 - loss: 2.1745 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2727 - loss: 2.1778
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2726 - loss: 2.1787
[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2728 - loss: 2.1801
[1m126/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2725 - loss: 2.1824
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2723 - loss: 2.1842 - val_accuracy: 0.3683 - val_loss: 2.0964
Epoch 25/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2500 - loss: 2.2066
[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2660 - loss: 2.2025 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2669 - loss: 2.1994
[1m 73/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2669 - loss: 2.1972
[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2680 - loss: 2.1946
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2689 - loss: 2.1921
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2695 - loss: 2.1905 - val_accuracy: 0.3566 - val_loss: 2.0918
Epoch 26/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.2656 - loss: 2.2029
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2729 - loss: 2.1835 
[1m 53/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2739 - loss: 2.1806
[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2751 - loss: 2.1759
[1m104/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2752 - loss: 2.1741
[1m129/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2754 - loss: 2.1737
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2756 - loss: 2.1733 - val_accuracy: 0.3639 - val_loss: 2.1220
Epoch 27/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.3359 - loss: 1.9706
[1m 23/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2925 - loss: 2.1448 
[1m 46/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2876 - loss: 2.1555
[1m 71/145[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2853 - loss: 2.1594
[1m 94/145[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2841 - loss: 2.1613
[1m116/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2828 - loss: 2.1634
[1m139/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.2820 - loss: 2.1641
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2818 - loss: 2.1642 - val_accuracy: 0.3716 - val_loss: 2.0782
Epoch 28/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.3047 - loss: 2.0288
[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2792 - loss: 2.1473 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2803 - loss: 2.1506
[1m 73/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2808 - loss: 2.1495
[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2815 - loss: 2.1480
[1m121/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2818 - loss: 2.1478
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.2820 - loss: 2.1479
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2821 - loss: 2.1479 - val_accuracy: 0.3611 - val_loss: 2.0782
Epoch 29/124

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[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2882 - loss: 2.1534
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[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2887 - loss: 2.1456
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Epoch 30/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2712 - loss: 2.1542 
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[1m106/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2821 - loss: 2.1339
[1m131/145[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.2833 - loss: 2.1329
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Epoch 31/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2889 - loss: 2.1152 
[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2869 - loss: 2.1156
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[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2873 - loss: 2.1212
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2873 - loss: 2.1223
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2875 - loss: 2.1225 - val_accuracy: 0.3774 - val_loss: 2.0462
Epoch 32/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2931 - loss: 2.1193 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2967 - loss: 2.1050
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2964 - loss: 2.1039
[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2962 - loss: 2.1035
[1m126/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2962 - loss: 2.1037
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2961 - loss: 2.1042 - val_accuracy: 0.3840 - val_loss: 2.0423
Epoch 33/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.3047 - loss: 2.0574
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2950 - loss: 2.1033 
[1m 48/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2946 - loss: 2.1038
[1m 72/145[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2950 - loss: 2.1059
[1m 96/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2952 - loss: 2.1071
[1m122/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2956 - loss: 2.1073
[1m144/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.2961 - loss: 2.1065
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2962 - loss: 2.1064 - val_accuracy: 0.3752 - val_loss: 2.0538
Epoch 34/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 25ms/step - accuracy: 0.3125 - loss: 2.0067
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3032 - loss: 2.0656 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3042 - loss: 2.0728
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3045 - loss: 2.0749
[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3036 - loss: 2.0774
[1m127/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3024 - loss: 2.0789
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3019 - loss: 2.0800 - val_accuracy: 0.3732 - val_loss: 2.0411
Epoch 35/124

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[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3038 - loss: 2.0735 
[1m 48/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3024 - loss: 2.0783
[1m 74/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3018 - loss: 2.0817
[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3019 - loss: 2.0831
[1m123/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3025 - loss: 2.0824
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3029 - loss: 2.0814 - val_accuracy: 0.3738 - val_loss: 2.0440
Epoch 36/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3169 - loss: 2.0315 
[1m 53/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3149 - loss: 2.0350
[1m 79/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3135 - loss: 2.0401
[1m104/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3121 - loss: 2.0446
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Epoch 37/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3132 - loss: 2.0833 
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[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3103 - loss: 2.0717
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3099 - loss: 2.0698
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Epoch 38/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3197 - loss: 2.0257 
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[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3149 - loss: 2.0431
[1m123/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3147 - loss: 2.0431
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3147 - loss: 2.0434 - val_accuracy: 0.3826 - val_loss: 2.0208
Epoch 39/124

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[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3199 - loss: 2.0270
[1m 72/145[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3201 - loss: 2.0226
[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3195 - loss: 2.0216
[1m122/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3186 - loss: 2.0221
[1m144/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.3182 - loss: 2.0223
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3182 - loss: 2.0223 - val_accuracy: 0.3895 - val_loss: 2.0111
Epoch 40/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3298 - loss: 2.0306 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3252 - loss: 2.0310
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3224 - loss: 2.0319
[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3213 - loss: 2.0304
[1m129/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3209 - loss: 2.0291
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3207 - loss: 2.0286 - val_accuracy: 0.3881 - val_loss: 2.0234
Epoch 41/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3323 - loss: 2.0143 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3327 - loss: 2.0110
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3315 - loss: 2.0124
[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3301 - loss: 2.0138
[1m126/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3288 - loss: 2.0154
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3281 - loss: 2.0166 - val_accuracy: 0.3846 - val_loss: 2.0083
Epoch 42/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3125 - loss: 2.0183 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3161 - loss: 2.0109
[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3158 - loss: 2.0097
[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3155 - loss: 2.0088
[1m129/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3153 - loss: 2.0091
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3154 - loss: 2.0090 - val_accuracy: 0.3840 - val_loss: 2.0117
Epoch 43/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3252 - loss: 2.0028 
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Epoch 44/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3438 - loss: 1.9652 
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[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3310 - loss: 1.9944
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Epoch 45/124

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[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3203 - loss: 1.9916
[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3218 - loss: 1.9924
[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3234 - loss: 1.9912
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Epoch 46/124

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[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3468 - loss: 1.9436
[1m 74/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3425 - loss: 1.9562
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3395 - loss: 1.9638
[1m126/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3376 - loss: 1.9692
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3370 - loss: 1.9708 - val_accuracy: 0.4000 - val_loss: 1.9927
Epoch 47/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3256 - loss: 1.9494 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3267 - loss: 1.9676
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3272 - loss: 1.9734
[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3274 - loss: 1.9754
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3279 - loss: 1.9764
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3285 - loss: 1.9763 - val_accuracy: 0.3969 - val_loss: 1.9869
Epoch 48/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.3594 - loss: 1.8637
[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3278 - loss: 1.9541 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3269 - loss: 1.9654
[1m 73/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3273 - loss: 1.9670
[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3286 - loss: 1.9663
[1m123/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3298 - loss: 1.9654
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3308 - loss: 1.9647 - val_accuracy: 0.4100 - val_loss: 1.9872
Epoch 49/124

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[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3263 - loss: 1.9680 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3345 - loss: 1.9557
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[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3397 - loss: 1.9479
[1m126/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3402 - loss: 1.9482
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Epoch 50/124

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Epoch 51/124

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Epoch 52/124

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[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3494 - loss: 1.9307
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Epoch 53/124

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[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3431 - loss: 1.9208
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[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3470 - loss: 1.9218
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Epoch 54/124

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[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3645 - loss: 1.9013 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3621 - loss: 1.9120
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[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3616 - loss: 1.9127
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[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3606 - loss: 1.9137 - val_accuracy: 0.4074 - val_loss: 1.9683
Epoch 55/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3395 - loss: 1.9490 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3439 - loss: 1.9347
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[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3476 - loss: 1.9233
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Epoch 56/124

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[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3650 - loss: 1.9114
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[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step 
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 11ms/step
Saved model to disk.

Accuracy capturado en la ejecución 5: 31.75 [%]
F1-score capturado en la ejecución 5: 31.19 [%]

=== EJECUCIÓN 6 ===

--- TRAIN (ejecución 6) ---

--- TEST (ejecución 6) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 409,231 (1.56 MB)
 Trainable params: 409,231 (1.56 MB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 6, 250)
(18469, 6, 250)

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9:42[0m 1s/step
[1m 62/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 826us/step
[1m125/578[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 814us/step
[1m192/578[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 791us/step
[1m259/578[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 781us/step
[1m325/578[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 778us/step
[1m388/578[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 781us/step
[1m451/578[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 784us/step
[1m511/578[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 791us/step
[1m577/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 788us/step
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 2ms/step

[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m62/86[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 820us/step
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step 
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 10ms/step

[1m  1/158[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step
[1m 62/158[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 830us/step
[1m126/158[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 811us/step
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 40.26 [%]
Global F1 score (validation) = 38.06 [%]
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[[2.1077888e-03 1.0195746e-03 1.3587984e-03 ... 3.6220569e-02
  2.8074209e-03 5.9294142e-04]
 [2.2993702e-03 1.3559960e-03 1.7848505e-03 ... 5.9681498e-02
  3.4968224e-03 8.0726383e-04]
 [8.7490294e-04 2.3776565e-04 5.3693028e-04 ... 1.1731334e-02
  1.0258325e-03 2.3818940e-04]
 ...
 [1.6972633e-01 3.6259830e-02 2.0355065e-01 ... 3.4107553e-04
  2.7197751e-01 7.8700222e-02]
 [1.9099161e-01 5.5318143e-02 1.9653109e-01 ... 5.9597992e-04
  2.2829977e-01 7.5389706e-02]
 [1.9003271e-01 3.8944837e-02 1.9225977e-01 ... 4.4909914e-04
  2.7596980e-01 6.6626057e-02]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 43.28 [%]
Global accuracy score (test) = 33.5 [%]
Global F1 score (train) = 42.28 [%]
Global F1 score (test) = 32.38 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.30      0.35      0.33       184
 CAMINAR CON MÓVIL O LIBRO       0.36      0.34      0.35       184
       CAMINAR USUAL SPEED       0.14      0.07      0.09       184
            CAMINAR ZIGZAG       0.11      0.05      0.07       184
          DE PIE BARRIENDO       0.27      0.15      0.19       184
   DE PIE DOBLANDO TOALLAS       0.37      0.41      0.39       184
    DE PIE MOVIENDO LIBROS       0.41      0.27      0.33       184
          DE PIE USANDO PC       0.24      0.22      0.23       184
        FASE REPOSO CON K5       0.42      0.74      0.54       184
INCREMENTAL CICLOERGOMETRO       0.58      0.62      0.60       184
           SENTADO LEYENDO       0.51      0.33      0.40       184
         SENTADO USANDO PC       0.15      0.14      0.14       184
      SENTADO VIENDO LA TV       0.39      0.35      0.37       184
   SUBIR Y BAJAR ESCALERAS       0.18      0.43      0.25       184
                    TROTAR       0.58      0.59      0.58       161

                  accuracy                           0.34      2737
                 macro avg       0.33      0.34      0.32      2737
              weighted avg       0.33      0.34      0.32      2737

2025-10-28 12:49:59.721025: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-28 12:49:59.732410: 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 00:00:1761652199.745996 1788350 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761652199.750376 1788350 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761652199.760722 1788350 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761652199.760749 1788350 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761652199.760760 1788350 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761652199.760762 1788350 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-28 12:49:59.764072: 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 00:00:1761652202.142733 1788350 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13749 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 409,231 (1.56 MB)
 Trainable params: 409,231 (1.56 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/124
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761652204.723218 1788451 service.cc:152] XLA service 0x7f6ac0003b80 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761652204.723290 1788451 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-28 12:50:04.781524: 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 00:00:1761652205.083132 1788451 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761652208.706340 1788451 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13:53[0m 6s/step - accuracy: 0.0547 - loss: 3.2430
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[1m 46/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.0715 - loss: 3.1752
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Epoch 2/124

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Epoch 3/124

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Epoch 4/124

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[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1304 - loss: 2.7782
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1306 - loss: 2.7771
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Epoch 5/124

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[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1368 - loss: 2.7268
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1375 - loss: 2.7243
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Epoch 6/124

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[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1445 - loss: 2.6855
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1454 - loss: 2.6821
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.1465 - loss: 2.6784 - val_accuracy: 0.2259 - val_loss: 2.3419
Epoch 7/124

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[1m104/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1595 - loss: 2.6245
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Epoch 8/124

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[1m121/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1735 - loss: 2.5492
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Epoch 9/124

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Epoch 10/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1931 - loss: 2.4851 
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[1m 96/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1912 - loss: 2.4901
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[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.1920 - loss: 2.4888 - val_accuracy: 0.3071 - val_loss: 2.2525
Epoch 11/124

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[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2066 - loss: 2.4952 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2022 - loss: 2.4814
[1m 75/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2009 - loss: 2.4749
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2000 - loss: 2.4712
[1m126/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1995 - loss: 2.4688
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.1991 - loss: 2.4677 - val_accuracy: 0.3061 - val_loss: 2.2386
Epoch 12/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.1797 - loss: 2.4053
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1893 - loss: 2.4690 
[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1908 - loss: 2.4659
[1m 75/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1927 - loss: 2.4625
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1948 - loss: 2.4588
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1966 - loss: 2.4555
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.1980 - loss: 2.4532 - val_accuracy: 0.3202 - val_loss: 2.2230
Epoch 13/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.1797 - loss: 2.3120
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2110 - loss: 2.3843 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2135 - loss: 2.3869
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[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2127 - loss: 2.3958
[1m123/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2126 - loss: 2.3987
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.2123 - loss: 2.4007
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2123 - loss: 2.4008 - val_accuracy: 0.3226 - val_loss: 2.1904
Epoch 14/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.1875 - loss: 2.3673
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2189 - loss: 2.3723 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2159 - loss: 2.3835
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2156 - loss: 2.3872
[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2158 - loss: 2.3871
[1m127/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2159 - loss: 2.3868
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Epoch 15/124

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[1m121/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2225 - loss: 2.3647
[1m143/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.2223 - loss: 2.3650
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Epoch 16/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2200 - loss: 2.3715 
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[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2257 - loss: 2.3536
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[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2266 - loss: 2.3493 - val_accuracy: 0.3441 - val_loss: 2.1601
Epoch 17/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.2344 - loss: 2.2794
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2385 - loss: 2.2992 
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[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2342 - loss: 2.3159
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[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2328 - loss: 2.3191 - val_accuracy: 0.3323 - val_loss: 2.1709
Epoch 18/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.2656 - loss: 2.3823
[1m 23/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2247 - loss: 2.3314 
[1m 48/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2293 - loss: 2.3220
[1m 73/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2316 - loss: 2.3174
[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2326 - loss: 2.3151
[1m123/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2333 - loss: 2.3131
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2340 - loss: 2.3110 - val_accuracy: 0.3405 - val_loss: 2.1567
Epoch 19/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.3594 - loss: 2.0548
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2635 - loss: 2.2520 
[1m 54/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2552 - loss: 2.2696
[1m 80/145[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2507 - loss: 2.2774
[1m104/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2487 - loss: 2.2811
[1m130/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2475 - loss: 2.2831
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2471 - loss: 2.2835 - val_accuracy: 0.3458 - val_loss: 2.1489
Epoch 20/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.2891 - loss: 2.3134
[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2511 - loss: 2.2611 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2464 - loss: 2.2656
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2462 - loss: 2.2657
[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2456 - loss: 2.2667
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2454 - loss: 2.2664
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2454 - loss: 2.2662 - val_accuracy: 0.3484 - val_loss: 2.1391
Epoch 21/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.2422 - loss: 2.2791
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2496 - loss: 2.2572 
[1m 53/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2498 - loss: 2.2548
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2502 - loss: 2.2534
[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2500 - loss: 2.2540
[1m128/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2499 - loss: 2.2544
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2502 - loss: 2.2542 - val_accuracy: 0.3542 - val_loss: 2.1311
Epoch 22/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2640 - loss: 2.2171 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2660 - loss: 2.2261
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2648 - loss: 2.2316
[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2642 - loss: 2.2344
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Epoch 23/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2680 - loss: 2.2035 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2692 - loss: 2.2103
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2691 - loss: 2.2141
[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2690 - loss: 2.2146
[1m119/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2687 - loss: 2.2151
[1m144/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.2684 - loss: 2.2157
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Epoch 24/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.3281 - loss: 2.1281
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2671 - loss: 2.1970 
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[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2644 - loss: 2.2062
[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2640 - loss: 2.2076
[1m123/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2644 - loss: 2.2079
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2649 - loss: 2.2075 - val_accuracy: 0.3578 - val_loss: 2.1182
Epoch 25/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2546 - loss: 2.2289 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2623 - loss: 2.2149
[1m 75/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2649 - loss: 2.2091
[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2663 - loss: 2.2053
[1m122/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2671 - loss: 2.2031
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2679 - loss: 2.2016 - val_accuracy: 0.3631 - val_loss: 2.1025
Epoch 26/124

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[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2727 - loss: 2.1810 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2742 - loss: 2.1772
[1m 74/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2751 - loss: 2.1768
[1m 96/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2755 - loss: 2.1767
[1m121/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2761 - loss: 2.1761
[1m144/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.2761 - loss: 2.1762
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2761 - loss: 2.1762 - val_accuracy: 0.3669 - val_loss: 2.1001
Epoch 27/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2668 - loss: 2.2224 
[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2703 - loss: 2.2056
[1m 73/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2709 - loss: 2.1995
[1m 97/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2716 - loss: 2.1955
[1m120/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2720 - loss: 2.1922
[1m144/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.2724 - loss: 2.1890
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2724 - loss: 2.1888 - val_accuracy: 0.3703 - val_loss: 2.0871
Epoch 28/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2684 - loss: 2.1717 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2734 - loss: 2.1648
[1m 74/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2763 - loss: 2.1604
[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2779 - loss: 2.1584
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2788 - loss: 2.1573
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2794 - loss: 2.1569 - val_accuracy: 0.3653 - val_loss: 2.1076
Epoch 29/124

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[1m 48/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2790 - loss: 2.1356
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[1m 95/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2789 - loss: 2.1407
[1m119/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2793 - loss: 2.1401
[1m142/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.2799 - loss: 2.1396
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2800 - loss: 2.1394 - val_accuracy: 0.3609 - val_loss: 2.0904
Epoch 30/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3002 - loss: 2.0940 
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Epoch 31/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2864 - loss: 2.1111 
[1m 53/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2863 - loss: 2.1196
[1m 80/145[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2873 - loss: 2.1218
[1m106/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2883 - loss: 2.1211
[1m132/145[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.2892 - loss: 2.1197
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2896 - loss: 2.1191 - val_accuracy: 0.3677 - val_loss: 2.0737
Epoch 32/124

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[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3077 - loss: 2.0949 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3058 - loss: 2.0887
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3037 - loss: 2.0886
[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3028 - loss: 2.0897
[1m129/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3015 - loss: 2.0918
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3008 - loss: 2.0931 - val_accuracy: 0.3715 - val_loss: 2.0548
Epoch 33/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2266 - loss: 2.2252
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2890 - loss: 2.1396 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2936 - loss: 2.1215
[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2966 - loss: 2.1083
[1m105/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2976 - loss: 2.1032
[1m132/145[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.2981 - loss: 2.1004
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2981 - loss: 2.0993 - val_accuracy: 0.3635 - val_loss: 2.0873
Epoch 34/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 25ms/step - accuracy: 0.3281 - loss: 1.9965
[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3111 - loss: 2.0828 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3117 - loss: 2.0765
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3120 - loss: 2.0748
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3117 - loss: 2.0744
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3113 - loss: 2.0747
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3108 - loss: 2.0752 - val_accuracy: 0.3697 - val_loss: 2.0743
Epoch 35/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 26ms/step - accuracy: 0.3047 - loss: 1.9998
[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2945 - loss: 2.1073 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3000 - loss: 2.0965
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Epoch 36/124

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[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3095 - loss: 2.0759
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Epoch 37/124

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[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3017 - loss: 2.0722
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Epoch 38/124

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[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3120 - loss: 2.0591
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[1m 97/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3139 - loss: 2.0546
[1m123/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3146 - loss: 2.0537
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Epoch 39/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3153 - loss: 2.0225 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3160 - loss: 2.0284
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3157 - loss: 2.0328
[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3158 - loss: 2.0343
[1m127/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3158 - loss: 2.0351
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3159 - loss: 2.0354 - val_accuracy: 0.3778 - val_loss: 2.0402
Epoch 40/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.3516 - loss: 1.9298
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3105 - loss: 2.0513 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3108 - loss: 2.0474
[1m 79/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3112 - loss: 2.0428
[1m104/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3123 - loss: 2.0398
[1m129/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3131 - loss: 2.0376
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3133 - loss: 2.0368 - val_accuracy: 0.3728 - val_loss: 2.0229
Epoch 41/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2891 - loss: 2.0738
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3096 - loss: 2.0375 
[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3122 - loss: 2.0313
[1m 75/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3140 - loss: 2.0291
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3150 - loss: 2.0285
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3162 - loss: 2.0268
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3168 - loss: 2.0256 - val_accuracy: 0.3740 - val_loss: 2.0219
Epoch 42/124

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Epoch 43/124

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Epoch 44/124

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Epoch 45/124

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[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3363 - loss: 1.9817
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Epoch 46/124

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[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3303 - loss: 1.9946
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3304 - loss: 1.9926
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Epoch 47/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3391 - loss: 1.9745 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3400 - loss: 1.9764
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[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3406 - loss: 1.9740
[1m126/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3405 - loss: 1.9727
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3403 - loss: 1.9721 - val_accuracy: 0.4016 - val_loss: 2.0110
Epoch 48/124

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[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3452 - loss: 1.9464
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Epoch 49/124

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[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3466 - loss: 1.9428
[1m128/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3454 - loss: 1.9439
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Epoch 50/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.3906 - loss: 1.8778
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[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3465 - loss: 1.9233
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[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3453 - loss: 1.9346 - val_accuracy: 0.3859 - val_loss: 2.0158
Epoch 51/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.4062 - loss: 1.8458
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3522 - loss: 1.9411 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3498 - loss: 1.9431
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[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3475 - loss: 1.9431
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3474 - loss: 1.9422
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3473 - loss: 1.9415 - val_accuracy: 0.3826 - val_loss: 2.0307
Epoch 52/124

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[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3470 - loss: 1.9400
[1m 74/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3469 - loss: 1.9393
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3466 - loss: 1.9383
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3469 - loss: 1.9370
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3471 - loss: 1.9362 - val_accuracy: 0.3905 - val_loss: 2.0126
Epoch 53/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.3359 - loss: 1.9964
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3468 - loss: 1.9183 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3465 - loss: 1.9159
[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3459 - loss: 1.9178
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3459 - loss: 1.9187
[1m126/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3458 - loss: 1.9204
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3460 - loss: 1.9206 - val_accuracy: 0.3867 - val_loss: 2.0378

[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:13[0m 864ms/step
[1m58/86[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 878us/step  
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step 
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 10ms/step
Saved model to disk.

Accuracy capturado en la ejecución 6: 33.5 [%]
F1-score capturado en la ejecución 6: 32.38 [%]

=== EJECUCIÓN 7 ===

--- TRAIN (ejecución 7) ---

--- TEST (ejecución 7) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 409,231 (1.56 MB)
 Trainable params: 409,231 (1.56 MB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 6, 250)
(18469, 6, 250)

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[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
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[1m  1/158[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step
[1m 57/158[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 900us/step
[1m120/158[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 844us/step
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 38.67 [%]
Global F1 score (validation) = 36.47 [%]
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[[0.00110896 0.00066367 0.00115897 ... 0.02499412 0.00178082 0.0003924 ]
 [0.00167101 0.00152432 0.0017849  ... 0.05783678 0.0032202  0.00053736]
 [0.0014458  0.00072327 0.00162679 ... 0.06313698 0.00214625 0.0008176 ]
 ...
 [0.15789035 0.0325967  0.2225856  ... 0.00035679 0.21189831 0.12930602]
 [0.17271128 0.05010813 0.2407846  ... 0.00030011 0.16459815 0.1421944 ]
 [0.18095103 0.03215947 0.21468945 ... 0.00038732 0.24110305 0.09391905]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 42.61 [%]
Global accuracy score (test) = 32.33 [%]
Global F1 score (train) = 41.38 [%]
Global F1 score (test) = 31.47 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.30      0.34      0.32       184
 CAMINAR CON MÓVIL O LIBRO       0.38      0.36      0.37       184
       CAMINAR USUAL SPEED       0.14      0.14      0.14       184
            CAMINAR ZIGZAG       0.13      0.08      0.10       184
          DE PIE BARRIENDO       0.29      0.18      0.23       184
   DE PIE DOBLANDO TOALLAS       0.29      0.36      0.32       184
    DE PIE MOVIENDO LIBROS       0.31      0.17      0.22       184
          DE PIE USANDO PC       0.25      0.28      0.27       184
        FASE REPOSO CON K5       0.42      0.74      0.54       184
INCREMENTAL CICLOERGOMETRO       0.50      0.63      0.56       184
           SENTADO LEYENDO       0.46      0.37      0.41       184
         SENTADO USANDO PC       0.26      0.16      0.20       184
      SENTADO VIENDO LA TV       0.40      0.27      0.32       184
   SUBIR Y BAJAR ESCALERAS       0.13      0.21      0.16       184
                    TROTAR       0.56      0.57      0.57       161

                  accuracy                           0.32      2737
                 macro avg       0.32      0.33      0.31      2737
              weighted avg       0.32      0.32      0.31      2737

2025-10-28 12:50:50.522441: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-28 12:50:50.533912: 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 00:00:1761652250.547322 1794340 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761652250.551503 1794340 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761652250.561742 1794340 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761652250.561768 1794340 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761652250.561772 1794340 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761652250.561775 1794340 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-28 12:50:50.565091: 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 00:00:1761652252.912439 1794340 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13749 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 409,231 (1.56 MB)
 Trainable params: 409,231 (1.56 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/124
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761652255.440245 1794453 service.cc:152] XLA service 0x71ca14023e80 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761652255.440315 1794453 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-28 12:50:55.499275: 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 00:00:1761652255.809924 1794453 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761652259.473904 1794453 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13:55[0m 6s/step - accuracy: 0.0781 - loss: 3.1331
[1m 22/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.0696 - loss: 3.2605  
[1m 47/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.0722 - loss: 3.2293
[1m 73/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.0746 - loss: 3.2023
[1m 95/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.0764 - loss: 3.1859
[1m119/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.0780 - loss: 3.1721
[1m144/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.0796 - loss: 3.1598
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 27ms/step - accuracy: 0.0797 - loss: 3.15942025-10-28 12:51:05.178293: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_316', 12 bytes spill stores, 12 bytes spill loads


[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m12s[0m 43ms/step - accuracy: 0.0797 - loss: 3.1589 - val_accuracy: 0.1763 - val_loss: 2.4939
Epoch 2/124

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[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.0962 - loss: 2.9910
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[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.0976 - loss: 2.9806
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.0977 - loss: 2.9767
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.0981 - loss: 2.9731 - val_accuracy: 0.1993 - val_loss: 2.4316
Epoch 3/124

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Epoch 4/124

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Epoch 5/124

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Epoch 6/124

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[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1443 - loss: 2.6891
[1m121/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1454 - loss: 2.6870
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Epoch 7/124

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[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1464 - loss: 2.6582 
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[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1550 - loss: 2.6416
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1556 - loss: 2.6412
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.1559 - loss: 2.6409 - val_accuracy: 0.2579 - val_loss: 2.3152
Epoch 8/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1580 - loss: 2.6492 
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[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1631 - loss: 2.6125
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1635 - loss: 2.6091
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.1637 - loss: 2.6066 - val_accuracy: 0.2631 - val_loss: 2.2988
Epoch 9/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1713 - loss: 2.5631 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1724 - loss: 2.5611
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[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1734 - loss: 2.5602
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[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.1743 - loss: 2.5584
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Epoch 10/124

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Epoch 11/124

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Epoch 12/124

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Epoch 13/124

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[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1953 - loss: 2.4457
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[1m 97/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1982 - loss: 2.4383
[1m123/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1987 - loss: 2.4358
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.1992 - loss: 2.4340 - val_accuracy: 0.3153 - val_loss: 2.2287
Epoch 14/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2114 - loss: 2.4157 
[1m 53/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2098 - loss: 2.4105
[1m 79/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2093 - loss: 2.4108
[1m105/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2090 - loss: 2.4110
[1m132/145[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.2085 - loss: 2.4116
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2083 - loss: 2.4118 - val_accuracy: 0.3359 - val_loss: 2.1882
Epoch 15/124

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[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2384 - loss: 2.3559 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2359 - loss: 2.3552
[1m 75/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2333 - loss: 2.3570
[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2308 - loss: 2.3597
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2288 - loss: 2.3616
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2279 - loss: 2.3625 - val_accuracy: 0.3375 - val_loss: 2.1824
Epoch 16/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.1953 - loss: 2.4712
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2141 - loss: 2.3839 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2150 - loss: 2.3806
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[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2162 - loss: 2.3747
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2162 - loss: 2.3730
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2164 - loss: 2.3717 - val_accuracy: 0.3373 - val_loss: 2.1969
Epoch 17/124

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[1m 48/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2201 - loss: 2.3295
[1m 74/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2220 - loss: 2.3329
[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2235 - loss: 2.3344
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2251 - loss: 2.3345
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2260 - loss: 2.3340 - val_accuracy: 0.3458 - val_loss: 2.1657
Epoch 18/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.1797 - loss: 2.4500
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2224 - loss: 2.3351 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2278 - loss: 2.3243
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[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2302 - loss: 2.3240
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2310 - loss: 2.3230
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Epoch 19/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2249 - loss: 2.3494 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2296 - loss: 2.3389
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2320 - loss: 2.3305
[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2336 - loss: 2.3251
[1m128/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2352 - loss: 2.3204
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Epoch 20/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2522 - loss: 2.2457 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2485 - loss: 2.2597
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2477 - loss: 2.2655
[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2470 - loss: 2.2682
[1m129/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2464 - loss: 2.2704
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2463 - loss: 2.2711 - val_accuracy: 0.3583 - val_loss: 2.1446
Epoch 21/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.3203 - loss: 2.1890
[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2693 - loss: 2.2430 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2599 - loss: 2.2503
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2574 - loss: 2.2531
[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2564 - loss: 2.2540
[1m127/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2559 - loss: 2.2542
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2555 - loss: 2.2547 - val_accuracy: 0.3494 - val_loss: 2.1241
Epoch 22/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.2891 - loss: 2.2281
[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2590 - loss: 2.2469 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2550 - loss: 2.2477
[1m 74/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2530 - loss: 2.2493
[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2521 - loss: 2.2495
[1m120/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2518 - loss: 2.2492
[1m144/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.2518 - loss: 2.2491
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2519 - loss: 2.2491 - val_accuracy: 0.3544 - val_loss: 2.1356
Epoch 23/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.2578 - loss: 2.1870
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2657 - loss: 2.2353 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2650 - loss: 2.2337
[1m 72/145[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2645 - loss: 2.2347
[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2638 - loss: 2.2342
[1m116/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2632 - loss: 2.2344
[1m141/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.2627 - loss: 2.2344
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2626 - loss: 2.2344 - val_accuracy: 0.3591 - val_loss: 2.1096
Epoch 24/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2605 - loss: 2.2287 
[1m 48/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2585 - loss: 2.2283
[1m 73/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2589 - loss: 2.2267
[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2593 - loss: 2.2250
[1m123/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2595 - loss: 2.2236
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2599 - loss: 2.2222 - val_accuracy: 0.3597 - val_loss: 2.1197
Epoch 25/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 29ms/step - accuracy: 0.3125 - loss: 2.2394
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2887 - loss: 2.2075 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2794 - loss: 2.2083
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2756 - loss: 2.2073
[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2743 - loss: 2.2064
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2739 - loss: 2.2048
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2737 - loss: 2.2037 - val_accuracy: 0.3677 - val_loss: 2.0990
Epoch 26/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2717 - loss: 2.2117 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2703 - loss: 2.2025
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2718 - loss: 2.1924
[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2723 - loss: 2.1882
[1m129/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2729 - loss: 2.1856
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2732 - loss: 2.1844 - val_accuracy: 0.3639 - val_loss: 2.1143
Epoch 27/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.2891 - loss: 2.1152
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2807 - loss: 2.1568 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2760 - loss: 2.1689
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2764 - loss: 2.1704
[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2770 - loss: 2.1715
[1m129/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2772 - loss: 2.1716
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2773 - loss: 2.1715 - val_accuracy: 0.3635 - val_loss: 2.0924
Epoch 28/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.2969 - loss: 2.1528
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2874 - loss: 2.1544 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2832 - loss: 2.1604
[1m 75/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2811 - loss: 2.1622
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2800 - loss: 2.1629
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2799 - loss: 2.1623
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2799 - loss: 2.1614 - val_accuracy: 0.3673 - val_loss: 2.0940
Epoch 29/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 25ms/step - accuracy: 0.3359 - loss: 2.0062
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3020 - loss: 2.1153 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2936 - loss: 2.1229
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2898 - loss: 2.1269
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2876 - loss: 2.1310
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2864 - loss: 2.1335
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2858 - loss: 2.1346 - val_accuracy: 0.3716 - val_loss: 2.0847
Epoch 30/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.2578 - loss: 2.1064
[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2835 - loss: 2.1058 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2858 - loss: 2.1156
[1m 74/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2868 - loss: 2.1201
[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2879 - loss: 2.1215
[1m122/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2886 - loss: 2.1217
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.2887 - loss: 2.1223
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2887 - loss: 2.1223 - val_accuracy: 0.3697 - val_loss: 2.0739
Epoch 31/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2920 - loss: 2.1067 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2923 - loss: 2.1128
[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2919 - loss: 2.1122
[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2918 - loss: 2.1125
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Epoch 32/124

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[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2958 - loss: 2.1120
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[1m 96/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2962 - loss: 2.1093
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Epoch 33/124

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[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2807 - loss: 2.1405 
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[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2916 - loss: 2.1149
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2931 - loss: 2.1112
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Epoch 34/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3046 - loss: 2.1064 
[1m 53/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3039 - loss: 2.1028
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3031 - loss: 2.1015
[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3030 - loss: 2.0983
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[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3030 - loss: 2.0944 - val_accuracy: 0.3705 - val_loss: 2.0492
Epoch 35/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2984 - loss: 2.1003 
[1m 53/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3025 - loss: 2.0962
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3026 - loss: 2.0953
[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3025 - loss: 2.0924
[1m129/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3031 - loss: 2.0890
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3034 - loss: 2.0877 - val_accuracy: 0.3713 - val_loss: 2.0505
Epoch 36/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3004 - loss: 2.0616 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3019 - loss: 2.0650
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[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3042 - loss: 2.0652
[1m126/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3042 - loss: 2.0661
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3044 - loss: 2.0664 - val_accuracy: 0.3802 - val_loss: 2.0658
Epoch 37/124

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[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3181 - loss: 2.0357 
[1m 48/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3141 - loss: 2.0429
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[1m 95/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3122 - loss: 2.0475
[1m120/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3122 - loss: 2.0478
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3118 - loss: 2.0488
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Epoch 38/124

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[1m 47/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3133 - loss: 2.0321
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Epoch 39/124

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[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3158 - loss: 2.0266
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3163 - loss: 2.0269
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Epoch 40/124

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[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3351 - loss: 2.0000 
[1m 48/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3319 - loss: 1.9998
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[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3279 - loss: 2.0079
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[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3260 - loss: 2.0106 - val_accuracy: 0.3836 - val_loss: 2.0395
Epoch 41/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 25ms/step - accuracy: 0.3359 - loss: 1.9247
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3263 - loss: 2.0003 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3272 - loss: 1.9988
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3260 - loss: 2.0006
[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3251 - loss: 2.0009
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3241 - loss: 2.0028
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3235 - loss: 2.0049 - val_accuracy: 0.3794 - val_loss: 2.0116
Epoch 42/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.3047 - loss: 2.1422
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3396 - loss: 2.0101 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3362 - loss: 2.0031
[1m 74/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3345 - loss: 2.0008
[1m 96/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3334 - loss: 1.9990
[1m117/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3321 - loss: 1.9994
[1m144/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.3312 - loss: 1.9996
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3312 - loss: 1.9996 - val_accuracy: 0.3838 - val_loss: 2.0233
Epoch 43/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3112 - loss: 2.0408 
[1m 53/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3213 - loss: 2.0263
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3229 - loss: 2.0186
[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3239 - loss: 2.0125
[1m129/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3247 - loss: 2.0085
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3251 - loss: 2.0067 - val_accuracy: 0.3873 - val_loss: 2.0296
Epoch 44/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3378 - loss: 1.9518 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3333 - loss: 1.9665
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[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3330 - loss: 1.9744
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3327 - loss: 1.9766
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3324 - loss: 1.9789 - val_accuracy: 0.3957 - val_loss: 2.0196
Epoch 45/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3237 - loss: 1.9952 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3235 - loss: 1.9970
[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3248 - loss: 1.9956
[1m105/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3260 - loss: 1.9931
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Epoch 46/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3197 - loss: 1.9743 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3221 - loss: 1.9694
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[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3225 - loss: 1.9724
[1m119/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3231 - loss: 1.9725
[1m141/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.3240 - loss: 1.9714
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Epoch 47/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3410 - loss: 1.9434 
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[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3390 - loss: 1.9531
[1m105/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3384 - loss: 1.9542
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Epoch 48/124

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[1m 24/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3498 - loss: 1.9401 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3438 - loss: 1.9509
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3424 - loss: 1.9540
[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3425 - loss: 1.9531
[1m126/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3424 - loss: 1.9525
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3421 - loss: 1.9524 - val_accuracy: 0.4016 - val_loss: 1.9978
Epoch 49/124

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[1m 24/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3354 - loss: 1.9382 
[1m 48/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3355 - loss: 1.9433
[1m 73/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3375 - loss: 1.9450
[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3395 - loss: 1.9450
[1m122/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3410 - loss: 1.9447
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3420 - loss: 1.9447 - val_accuracy: 0.3903 - val_loss: 1.9999
Epoch 50/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3494 - loss: 1.9352 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3484 - loss: 1.9355
[1m 75/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3475 - loss: 1.9347
[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3469 - loss: 1.9342
[1m126/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3462 - loss: 1.9351
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3458 - loss: 1.9357 - val_accuracy: 0.4000 - val_loss: 1.9792
Epoch 51/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3458 - loss: 1.9286 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3468 - loss: 1.9271
[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3469 - loss: 1.9267
[1m106/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3464 - loss: 1.9274
[1m131/145[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.3465 - loss: 1.9275
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3465 - loss: 1.9274 - val_accuracy: 0.3965 - val_loss: 1.9946
Epoch 52/124

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[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3592 - loss: 1.9213
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Epoch 53/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3626 - loss: 1.9346 
[1m 54/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3598 - loss: 1.9314
[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3581 - loss: 1.9276
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Epoch 54/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3540 - loss: 1.8931 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3590 - loss: 1.8928
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3599 - loss: 1.8944
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Epoch 55/124

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[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3587 - loss: 1.8927
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[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3587 - loss: 1.8938
[1m121/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3589 - loss: 1.8940
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3594 - loss: 1.8937
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3594 - loss: 1.8937 - val_accuracy: 0.4004 - val_loss: 1.9686
Epoch 56/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3565 - loss: 1.9186 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3625 - loss: 1.9027
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3639 - loss: 1.8984
[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3640 - loss: 1.8967
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3638 - loss: 1.8954
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3635 - loss: 1.8950 - val_accuracy: 0.3941 - val_loss: 2.0069
Epoch 57/124

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[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3599 - loss: 1.9258 
[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3646 - loss: 1.9080
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[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3667 - loss: 1.8950
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3670 - loss: 1.8917
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3671 - loss: 1.8899 - val_accuracy: 0.3983 - val_loss: 1.9665
Epoch 58/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3768 - loss: 1.8426 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3775 - loss: 1.8493
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[1m104/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3744 - loss: 1.8631
[1m129/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3736 - loss: 1.8660
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Epoch 59/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.3047 - loss: 1.8986
[1m 23/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3533 - loss: 1.8884 
[1m 48/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3611 - loss: 1.8783
[1m 74/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3637 - loss: 1.8766
[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3647 - loss: 1.8767
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3650 - loss: 1.8774
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3652 - loss: 1.8775 - val_accuracy: 0.4092 - val_loss: 1.9551
Epoch 60/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.3281 - loss: 1.9014
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[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3743 - loss: 1.8464
[1m 74/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3740 - loss: 1.8480
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3732 - loss: 1.8500
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3723 - loss: 1.8516
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Epoch 61/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 28ms/step - accuracy: 0.4453 - loss: 1.8022
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[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3723 - loss: 1.8702
[1m104/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3728 - loss: 1.8663
[1m130/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3735 - loss: 1.8638
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3739 - loss: 1.8630 - val_accuracy: 0.4074 - val_loss: 1.9681
Epoch 62/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.3047 - loss: 1.8495
[1m 28/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3738 - loss: 1.8208 
[1m 54/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3792 - loss: 1.8249
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[1m106/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3789 - loss: 1.8358
[1m132/145[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.3788 - loss: 1.8380
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3786 - loss: 1.8389 - val_accuracy: 0.4074 - val_loss: 1.9608
Epoch 63/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.4453 - loss: 1.8027
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4045 - loss: 1.8300 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3992 - loss: 1.8256
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[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3918 - loss: 1.8303
[1m127/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3896 - loss: 1.8324
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3884 - loss: 1.8334 - val_accuracy: 0.4104 - val_loss: 1.9973
Epoch 64/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.4297 - loss: 1.7711
[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3764 - loss: 1.8325 
[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3763 - loss: 1.8338
[1m 74/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3779 - loss: 1.8331
[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3791 - loss: 1.8334
[1m123/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3796 - loss: 1.8346
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3799 - loss: 1.8356 - val_accuracy: 0.4018 - val_loss: 1.9855

[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:14[0m 872ms/step
[1m56/86[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 914us/step  
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step 
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 11ms/step
Saved model to disk.

Accuracy capturado en la ejecución 7: 32.33 [%]
F1-score capturado en la ejecución 7: 31.47 [%]

=== EJECUCIÓN 8 ===

--- TRAIN (ejecución 8) ---

--- TEST (ejecución 8) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 409,231 (1.56 MB)
 Trainable params: 409,231 (1.56 MB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 6, 250)
(18469, 6, 250)

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9:40[0m 1s/step
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[1m234/578[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 866us/step
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[1m353/578[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 860us/step
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[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m56/86[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 917us/step
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step 
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 10ms/step

[1m  1/158[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step
[1m 58/158[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 880us/step
[1m119/158[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 850us/step
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 40.18 [%]
Global F1 score (validation) = 38.39 [%]
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[[2.9188832e-03 1.3450567e-03 2.5602202e-03 ... 3.0679751e-02
  4.4433922e-03 7.0402201e-04]
 [1.6590640e-03 1.8083138e-03 2.5670480e-03 ... 6.6911027e-02
  4.1289493e-03 6.7934848e-04]
 [7.1869261e-04 2.5829489e-04 8.3069521e-04 ... 1.2308004e-02
  1.2039257e-03 2.3858507e-04]
 ...
 [1.8126087e-01 4.9602896e-02 2.3880972e-01 ... 3.6359497e-04
  2.3860422e-01 5.4593626e-02]
 [1.5688746e-01 6.4018935e-02 2.1096687e-01 ... 9.9188893e-04
  1.7480928e-01 8.8032775e-02]
 [1.4943591e-01 3.7532516e-02 2.2201723e-01 ... 3.4343323e-04
  2.6197234e-01 8.0738582e-02]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 46.91 [%]
Global accuracy score (test) = 33.65 [%]
Global F1 score (train) = 46.42 [%]
Global F1 score (test) = 32.86 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.31      0.47      0.37       184
 CAMINAR CON MÓVIL O LIBRO       0.42      0.33      0.37       184
       CAMINAR USUAL SPEED       0.20      0.16      0.18       184
            CAMINAR ZIGZAG       0.14      0.11      0.12       184
          DE PIE BARRIENDO       0.30      0.23      0.26       184
   DE PIE DOBLANDO TOALLAS       0.33      0.40      0.36       184
    DE PIE MOVIENDO LIBROS       0.33      0.22      0.27       184
          DE PIE USANDO PC       0.21      0.21      0.21       184
        FASE REPOSO CON K5       0.42      0.74      0.54       184
INCREMENTAL CICLOERGOMETRO       0.55      0.60      0.57       184
           SENTADO LEYENDO       0.42      0.35      0.38       184
         SENTADO USANDO PC       0.17      0.10      0.13       184
      SENTADO VIENDO LA TV       0.45      0.38      0.41       184
   SUBIR Y BAJAR ESCALERAS       0.14      0.20      0.17       184
                    TROTAR       0.62      0.58      0.60       161

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

2025-10-28 12:51:46.036821: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-28 12:51:46.048297: 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 00:00:1761652306.061913 1801350 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761652306.066250 1801350 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761652306.076340 1801350 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761652306.076366 1801350 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761652306.076368 1801350 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761652306.076371 1801350 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-28 12:51:46.079757: 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 00:00:1761652308.451679 1801350 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13749 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 409,231 (1.56 MB)
 Trainable params: 409,231 (1.56 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/124
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761652311.004312 1801455 service.cc:152] XLA service 0x7ca5e0002760 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761652311.004362 1801455 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-28 12:51:51.058322: 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 00:00:1761652311.353363 1801455 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761652315.071185 1801455 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m14:04[0m 6s/step - accuracy: 0.0703 - loss: 3.1290
[1m 23/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.0716 - loss: 3.1510  
[1m 47/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.0742 - loss: 3.1489
[1m 71/145[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.0761 - loss: 3.1390
[1m 96/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.0777 - loss: 3.1300
[1m120/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.0789 - loss: 3.1220
[1m144/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.0798 - loss: 3.1153
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 27ms/step - accuracy: 0.0798 - loss: 3.11502025-10-28 12:52:00.750994: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_316', 12 bytes spill stores, 12 bytes spill loads


[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m12s[0m 43ms/step - accuracy: 0.0798 - loss: 3.1148 - val_accuracy: 0.1882 - val_loss: 2.4601
Epoch 2/124

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Epoch 3/124

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Epoch 4/124

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Epoch 5/124

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[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1345 - loss: 2.7346
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1357 - loss: 2.7312
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Epoch 6/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1341 - loss: 2.7046 
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[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1447 - loss: 2.6763
[1m122/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1462 - loss: 2.6727
[1m143/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.1473 - loss: 2.6700
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Epoch 7/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1570 - loss: 2.6574 
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[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1595 - loss: 2.6441
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1600 - loss: 2.6387
[1m127/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1603 - loss: 2.6335
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.1603 - loss: 2.6311 - val_accuracy: 0.2450 - val_loss: 2.2912
Epoch 8/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1660 - loss: 2.5953 
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[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1663 - loss: 2.5899
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Epoch 9/124

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Epoch 10/124

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Epoch 11/124

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[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1871 - loss: 2.4877
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1877 - loss: 2.4855
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.1884 - loss: 2.4838 - val_accuracy: 0.3061 - val_loss: 2.2166
Epoch 12/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1895 - loss: 2.4293 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1927 - loss: 2.4394
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[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1962 - loss: 2.4427
[1m126/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1972 - loss: 2.4411
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.1979 - loss: 2.4401 - val_accuracy: 0.3180 - val_loss: 2.2040
Epoch 13/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2071 - loss: 2.3922 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2028 - loss: 2.3995
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[1m 97/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2029 - loss: 2.4057
[1m117/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2032 - loss: 2.4072
[1m138/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.2038 - loss: 2.4075
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2040 - loss: 2.4075 - val_accuracy: 0.3262 - val_loss: 2.2013
Epoch 14/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2028 - loss: 2.4234 
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[1m 75/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2145 - loss: 2.3970
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2158 - loss: 2.3931
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2166 - loss: 2.3913
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2170 - loss: 2.3901 - val_accuracy: 0.3282 - val_loss: 2.1839
Epoch 15/124

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[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2318 - loss: 2.3562 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2259 - loss: 2.3653
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[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2209 - loss: 2.3740
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[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2205 - loss: 2.3734 - val_accuracy: 0.3490 - val_loss: 2.1750
Epoch 16/124

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[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2372 - loss: 2.3410 
[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2369 - loss: 2.3398
[1m 75/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2360 - loss: 2.3371
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2349 - loss: 2.3371
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[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2331 - loss: 2.3374 - val_accuracy: 0.3389 - val_loss: 2.1715
Epoch 17/124

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[1m 24/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2195 - loss: 2.3736 
[1m 48/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2236 - loss: 2.3615
[1m 72/145[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2267 - loss: 2.3522
[1m 97/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2281 - loss: 2.3470
[1m122/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2292 - loss: 2.3426
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.2298 - loss: 2.3396
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2298 - loss: 2.3395 - val_accuracy: 0.3413 - val_loss: 2.1556
Epoch 18/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 28ms/step - accuracy: 0.2188 - loss: 2.2093
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2248 - loss: 2.3023 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2295 - loss: 2.3119
[1m 72/145[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2312 - loss: 2.3140
[1m 97/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2318 - loss: 2.3151
[1m119/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2323 - loss: 2.3151
[1m143/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.2327 - loss: 2.3146
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2328 - loss: 2.3145 - val_accuracy: 0.3419 - val_loss: 2.1518
Epoch 19/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2266 - loss: 2.3048
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2206 - loss: 2.3201 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2281 - loss: 2.3091
[1m 75/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2323 - loss: 2.3043
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2349 - loss: 2.3023
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2367 - loss: 2.3008
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2378 - loss: 2.2995 - val_accuracy: 0.3450 - val_loss: 2.1327
Epoch 20/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.2500 - loss: 2.2691
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2400 - loss: 2.2978 
[1m 53/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2421 - loss: 2.2926
[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2447 - loss: 2.2868
[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2469 - loss: 2.2813
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2478 - loss: 2.2782
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2486 - loss: 2.2759 - val_accuracy: 0.3460 - val_loss: 2.1415
Epoch 21/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 25ms/step - accuracy: 0.2969 - loss: 2.2816
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2607 - loss: 2.2649 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2613 - loss: 2.2585
[1m 74/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2601 - loss: 2.2583
[1m 97/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2589 - loss: 2.2584
[1m122/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2584 - loss: 2.2578
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2583 - loss: 2.2571 - val_accuracy: 0.3500 - val_loss: 2.1201
Epoch 22/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2656 - loss: 2.3031
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2566 - loss: 2.2578 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2585 - loss: 2.2522
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2600 - loss: 2.2460
[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2604 - loss: 2.2429
[1m126/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2608 - loss: 2.2410
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2608 - loss: 2.2402 - val_accuracy: 0.3536 - val_loss: 2.1096
Epoch 23/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2422 - loss: 2.2002
[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2634 - loss: 2.2173 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2656 - loss: 2.2117
[1m 75/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2671 - loss: 2.2112
[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2674 - loss: 2.2110
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2671 - loss: 2.2117
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2667 - loss: 2.2122 - val_accuracy: 0.3520 - val_loss: 2.1126
Epoch 24/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.2422 - loss: 2.2085
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2693 - loss: 2.2079 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2692 - loss: 2.2047
[1m 75/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2696 - loss: 2.2037
[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2702 - loss: 2.2039
[1m122/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2706 - loss: 2.2041
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2707 - loss: 2.2044 - val_accuracy: 0.3496 - val_loss: 2.1162
Epoch 25/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.2891 - loss: 2.1674
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2788 - loss: 2.1639 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2780 - loss: 2.1695
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2791 - loss: 2.1723
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2798 - loss: 2.1749
[1m122/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2796 - loss: 2.1777
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2792 - loss: 2.1796 - val_accuracy: 0.3494 - val_loss: 2.1148
Epoch 26/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2734 - loss: 2.1657
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2750 - loss: 2.1705 
[1m 53/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2796 - loss: 2.1680
[1m 79/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2803 - loss: 2.1686
[1m105/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2802 - loss: 2.1694
[1m131/145[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.2800 - loss: 2.1703
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2796 - loss: 2.1711 - val_accuracy: 0.3508 - val_loss: 2.1016
Epoch 27/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2969 - loss: 1.9913
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2788 - loss: 2.1583 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2804 - loss: 2.1600
[1m 75/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2790 - loss: 2.1622
[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2781 - loss: 2.1644
[1m126/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2775 - loss: 2.1656
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2775 - loss: 2.1656 - val_accuracy: 0.3562 - val_loss: 2.0895
Epoch 28/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.2344 - loss: 2.1553
[1m 24/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2663 - loss: 2.1849 
[1m 47/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2742 - loss: 2.1672
[1m 70/145[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2767 - loss: 2.1596
[1m 96/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2775 - loss: 2.1570
[1m121/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2782 - loss: 2.1555
[1m144/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.2786 - loss: 2.1545
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2787 - loss: 2.1544 - val_accuracy: 0.3581 - val_loss: 2.0909
Epoch 29/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 25ms/step - accuracy: 0.2031 - loss: 2.2500
[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2697 - loss: 2.1515 
[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2744 - loss: 2.1517
[1m 73/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2789 - loss: 2.1503
[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2809 - loss: 2.1493
[1m123/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2821 - loss: 2.1478
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2831 - loss: 2.1463 - val_accuracy: 0.3633 - val_loss: 2.0962
Epoch 30/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2933 - loss: 2.1427 
[1m 53/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2928 - loss: 2.1352
[1m 79/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2927 - loss: 2.1323
[1m104/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2924 - loss: 2.1314
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Epoch 31/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2922 - loss: 2.1582 
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[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2912 - loss: 2.1361
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Epoch 32/124

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[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2933 - loss: 2.1252
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[1m 96/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2934 - loss: 2.1175
[1m117/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2934 - loss: 2.1154
[1m142/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.2937 - loss: 2.1134
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Epoch 33/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3069 - loss: 2.0443 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3023 - loss: 2.0660
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3023 - loss: 2.0716
[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3017 - loss: 2.0750
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3011 - loss: 2.0780
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3005 - loss: 2.0802 - val_accuracy: 0.3768 - val_loss: 2.0556
Epoch 34/124

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[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3129 - loss: 2.0903 
[1m 46/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3122 - loss: 2.0833
[1m 72/145[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3101 - loss: 2.0804
[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3080 - loss: 2.0808
[1m123/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3065 - loss: 2.0814
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3056 - loss: 2.0819 - val_accuracy: 0.3842 - val_loss: 2.0717
Epoch 35/124

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[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3202 - loss: 2.0180 
[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3146 - loss: 2.0419
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[1m 97/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3119 - loss: 2.0542
[1m122/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3105 - loss: 2.0578
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3098 - loss: 2.0597
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3098 - loss: 2.0598 - val_accuracy: 0.3881 - val_loss: 2.0534
Epoch 36/124

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[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3047 - loss: 2.0625 
[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3051 - loss: 2.0652
[1m 74/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3059 - loss: 2.0648
[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3067 - loss: 2.0631
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3071 - loss: 2.0621
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Epoch 37/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2891 - loss: 2.0597
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[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3053 - loss: 2.0580
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3063 - loss: 2.0565
[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3070 - loss: 2.0558
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Epoch 38/124

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[1m 96/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3166 - loss: 2.0496
[1m118/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3173 - loss: 2.0464
[1m139/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.3178 - loss: 2.0444
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3179 - loss: 2.0438 - val_accuracy: 0.3784 - val_loss: 2.0566
Epoch 39/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.3047 - loss: 2.0512
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3054 - loss: 2.0431 
[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3112 - loss: 2.0363
[1m 75/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3122 - loss: 2.0384
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3138 - loss: 2.0375
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3151 - loss: 2.0362
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3156 - loss: 2.0355 - val_accuracy: 0.3899 - val_loss: 2.0338
Epoch 40/124

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[1m 24/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3243 - loss: 2.0084 
[1m 47/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3253 - loss: 2.0032
[1m 73/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3244 - loss: 2.0042
[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3241 - loss: 2.0057
[1m120/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3239 - loss: 2.0071
[1m144/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.3235 - loss: 2.0090
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3235 - loss: 2.0091 - val_accuracy: 0.3935 - val_loss: 2.0331
Epoch 41/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.2656 - loss: 2.0276
[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3179 - loss: 2.0187 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3225 - loss: 2.0168
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3257 - loss: 2.0132
[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3268 - loss: 2.0129
[1m127/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3271 - loss: 2.0129
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3270 - loss: 2.0130 - val_accuracy: 0.3855 - val_loss: 2.0512
Epoch 42/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.3203 - loss: 2.0144
[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3074 - loss: 2.0552 
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[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3190 - loss: 2.0271
[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3204 - loss: 2.0223
[1m127/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3212 - loss: 2.0195
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3215 - loss: 2.0176 - val_accuracy: 0.3929 - val_loss: 2.0256
Epoch 43/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3440 - loss: 1.9403 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3379 - loss: 1.9593
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[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3335 - loss: 1.9750
[1m123/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3322 - loss: 1.9792
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Epoch 44/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.3516 - loss: 1.9549
[1m 28/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3250 - loss: 1.9857 
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[1m 79/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3270 - loss: 1.9905
[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3270 - loss: 1.9908
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Epoch 45/124

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[1m 23/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3360 - loss: 1.9644 
[1m 48/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3368 - loss: 1.9675
[1m 72/145[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3367 - loss: 1.9690
[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3357 - loss: 1.9706
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3352 - loss: 1.9711
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3354 - loss: 1.9715 - val_accuracy: 0.4044 - val_loss: 2.0043
Epoch 46/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3471 - loss: 1.9365 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3446 - loss: 1.9468
[1m 74/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3430 - loss: 1.9526
[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3416 - loss: 1.9571
[1m122/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3408 - loss: 1.9592
[1m144/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.3407 - loss: 1.9602
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Epoch 47/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.3359 - loss: 2.0314
[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3384 - loss: 1.9764 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3361 - loss: 1.9742
[1m 75/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3363 - loss: 1.9716
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3362 - loss: 1.9708
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3366 - loss: 1.9690
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3370 - loss: 1.9674 - val_accuracy: 0.3917 - val_loss: 2.0143
Epoch 48/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.3750 - loss: 2.0133
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3542 - loss: 1.9395 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3451 - loss: 1.9434
[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3438 - loss: 1.9412
[1m104/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3443 - loss: 1.9402
[1m127/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3448 - loss: 1.9400
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3448 - loss: 1.9406 - val_accuracy: 0.4082 - val_loss: 1.9956
Epoch 49/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.3438 - loss: 1.8527
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3449 - loss: 1.9211 
[1m 53/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3439 - loss: 1.9310
[1m 80/145[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3432 - loss: 1.9367
[1m105/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3440 - loss: 1.9377
[1m131/145[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.3445 - loss: 1.9381
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3447 - loss: 1.9383 - val_accuracy: 0.4002 - val_loss: 2.0108
Epoch 50/124

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[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3343 - loss: 1.9425 
[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3380 - loss: 1.9356
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[1m 96/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3420 - loss: 1.9358
[1m119/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3429 - loss: 1.9361
[1m142/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.3437 - loss: 1.9361
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3439 - loss: 1.9362 - val_accuracy: 0.4052 - val_loss: 1.9855
Epoch 51/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.3359 - loss: 1.9184
[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3524 - loss: 1.9064 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3503 - loss: 1.9081
[1m 75/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3507 - loss: 1.9084
[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3511 - loss: 1.9098
[1m123/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3513 - loss: 1.9117
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3513 - loss: 1.9136 - val_accuracy: 0.3951 - val_loss: 2.0221
Epoch 52/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.3359 - loss: 1.9522
[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3508 - loss: 1.9096 
[1m 48/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3496 - loss: 1.9137
[1m 74/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3505 - loss: 1.9146
[1m 96/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3514 - loss: 1.9150
[1m120/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3519 - loss: 1.9149
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3522 - loss: 1.9148
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3522 - loss: 1.9148 - val_accuracy: 0.4036 - val_loss: 2.0042
Epoch 53/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.3516 - loss: 1.9769
[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3498 - loss: 1.9111 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3535 - loss: 1.9084
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3546 - loss: 1.9077
[1m104/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3550 - loss: 1.9074
[1m131/145[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.3550 - loss: 1.9081
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3551 - loss: 1.9083 - val_accuracy: 0.4020 - val_loss: 2.0200
Epoch 54/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.4375 - loss: 1.7949
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3759 - loss: 1.8681 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3661 - loss: 1.8862
[1m 74/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3623 - loss: 1.8957
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3604 - loss: 1.8994
[1m126/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3598 - loss: 1.9003
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3597 - loss: 1.9006 - val_accuracy: 0.4054 - val_loss: 2.0152
Epoch 55/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.3906 - loss: 1.9250
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3643 - loss: 1.8660 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3642 - loss: 1.8738
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3641 - loss: 1.8750
[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3632 - loss: 1.8775
[1m126/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3627 - loss: 1.8796
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3626 - loss: 1.8806 - val_accuracy: 0.4066 - val_loss: 2.0035

[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:16[0m 897ms/step
[1m56/86[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 910us/step  
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step 
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 11ms/step
Saved model to disk.

Accuracy capturado en la ejecución 8: 33.65 [%]
F1-score capturado en la ejecución 8: 32.86 [%]

=== EJECUCIÓN 9 ===

--- TRAIN (ejecución 9) ---

--- TEST (ejecución 9) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 409,231 (1.56 MB)
 Trainable params: 409,231 (1.56 MB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 6, 250)
(18469, 6, 250)

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[1m63/86[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 811us/step
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[1m  1/158[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 17ms/step
[1m 57/158[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 903us/step
[1m111/158[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 919us/step
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 40.66 [%]
Global F1 score (validation) = 38.98 [%]
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[[1.5552506e-03 6.7127484e-04 1.4906519e-03 ... 3.9304338e-02
  1.8558400e-03 6.8677182e-04]
 [2.2552656e-03 1.3625659e-03 2.4062784e-03 ... 7.4884750e-02
  3.0176803e-03 1.0091704e-03]
 [5.2441045e-04 1.8780137e-04 5.6862802e-04 ... 2.0474121e-02
  6.1128684e-04 2.6030705e-04]
 ...
 [1.9167010e-01 5.7013657e-02 2.0932107e-01 ... 5.7211524e-04
  2.2414196e-01 6.7207314e-02]
 [1.7796710e-01 7.1964979e-02 2.1056716e-01 ... 7.1024947e-04
  1.8819188e-01 8.9676574e-02]
 [1.9336538e-01 4.8221435e-02 1.9352712e-01 ... 6.6929765e-04
  2.3911315e-01 6.9448128e-02]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 44.44 [%]
Global accuracy score (test) = 33.69 [%]
Global F1 score (train) = 44.39 [%]
Global F1 score (test) = 32.97 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.33      0.48      0.39       184
 CAMINAR CON MÓVIL O LIBRO       0.41      0.30      0.35       184
       CAMINAR USUAL SPEED       0.16      0.05      0.07       184
            CAMINAR ZIGZAG       0.18      0.17      0.18       184
          DE PIE BARRIENDO       0.23      0.22      0.23       184
   DE PIE DOBLANDO TOALLAS       0.37      0.35      0.36       184
    DE PIE MOVIENDO LIBROS       0.35      0.20      0.26       184
          DE PIE USANDO PC       0.23      0.20      0.21       184
        FASE REPOSO CON K5       0.42      0.74      0.54       184
INCREMENTAL CICLOERGOMETRO       0.63      0.62      0.62       184
           SENTADO LEYENDO       0.35      0.37      0.36       184
         SENTADO USANDO PC       0.16      0.14      0.15       184
      SENTADO VIENDO LA TV       0.37      0.39      0.38       184
   SUBIR Y BAJAR ESCALERAS       0.17      0.28      0.21       184
                    TROTAR       0.76      0.56      0.65       161

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

2025-10-28 12:52:37.848356: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-28 12:52:37.859721: 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 00:00:1761652357.872908 1807507 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761652357.877138 1807507 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761652357.887024 1807507 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761652357.887047 1807507 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761652357.887050 1807507 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761652357.887053 1807507 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-28 12:52:37.890320: 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 00:00:1761652360.281511 1807507 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13749 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 409,231 (1.56 MB)
 Trainable params: 409,231 (1.56 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/124
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761652362.854172 1807632 service.cc:152] XLA service 0x7f83d8012b30 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761652362.854242 1807632 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-28 12:52:42.911956: 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 00:00:1761652363.202868 1807632 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761652366.908181 1807632 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m14:03[0m 6s/step - accuracy: 0.1250 - loss: 3.1411
[1m 22/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.0882 - loss: 3.1422  
[1m 48/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.0841 - loss: 3.1464
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[1m 94/145[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.0853 - loss: 3.1289
[1m119/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.0864 - loss: 3.1188
[1m143/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.0873 - loss: 3.1104
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 27ms/step - accuracy: 0.0874 - loss: 3.10972025-10-28 12:52:52.651656: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_316', 12 bytes spill stores, 12 bytes spill loads


[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m12s[0m 44ms/step - accuracy: 0.0874 - loss: 3.1094 - val_accuracy: 0.1721 - val_loss: 2.5520
Epoch 2/124

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[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.0942 - loss: 2.9753 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.0988 - loss: 2.9696
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1002 - loss: 2.9649
[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1008 - loss: 2.9615
[1m126/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1013 - loss: 2.9584
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Epoch 3/124

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Epoch 4/124

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Epoch 5/124

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Epoch 6/124

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[1m 96/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1538 - loss: 2.6552
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Epoch 7/124

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[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1657 - loss: 2.5909
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[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.1646 - loss: 2.5941 - val_accuracy: 0.2631 - val_loss: 2.3123
Epoch 8/124

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[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1715 - loss: 2.5727
[1m123/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1713 - loss: 2.5708
[1m144/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.1714 - loss: 2.5688
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Epoch 9/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1774 - loss: 2.5647 
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Epoch 10/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1692 - loss: 2.5611 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1720 - loss: 2.5428
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1751 - loss: 2.5312
[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1766 - loss: 2.5269
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[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.1786 - loss: 2.5216 - val_accuracy: 0.2893 - val_loss: 2.2536
Epoch 11/124

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[1m 24/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2004 - loss: 2.4450 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1963 - loss: 2.4594
[1m 74/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1944 - loss: 2.4648
[1m 97/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1937 - loss: 2.4664
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[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.1936 - loss: 2.4664 - val_accuracy: 0.3208 - val_loss: 2.2325
Epoch 12/124

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[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2028 - loss: 2.4254 
[1m 48/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2058 - loss: 2.4229
[1m 72/145[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2069 - loss: 2.4238
[1m 96/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2073 - loss: 2.4254
[1m121/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2072 - loss: 2.4266
[1m144/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.2071 - loss: 2.4271
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2071 - loss: 2.4271 - val_accuracy: 0.3264 - val_loss: 2.2148
Epoch 13/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.2031 - loss: 2.3157
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1881 - loss: 2.4102 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1954 - loss: 2.4104
[1m 74/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2009 - loss: 2.4065
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2035 - loss: 2.4049
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2048 - loss: 2.4041
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2055 - loss: 2.4039 - val_accuracy: 0.3220 - val_loss: 2.2012
Epoch 14/124

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[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2211 - loss: 2.3741 
[1m 48/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2179 - loss: 2.3798
[1m 72/145[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2156 - loss: 2.3843
[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2146 - loss: 2.3865
[1m122/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2147 - loss: 2.3861
[1m144/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.2153 - loss: 2.3849
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2154 - loss: 2.3847 - val_accuracy: 0.3407 - val_loss: 2.1830
Epoch 15/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.1641 - loss: 2.3576
[1m 24/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2150 - loss: 2.3609 
[1m 48/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2210 - loss: 2.3586
[1m 72/145[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2241 - loss: 2.3578
[1m 97/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2251 - loss: 2.3585
[1m123/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2255 - loss: 2.3590
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2257 - loss: 2.3587 - val_accuracy: 0.3270 - val_loss: 2.1792
Epoch 16/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.1328 - loss: 2.4890
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2260 - loss: 2.3302 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2290 - loss: 2.3263
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2286 - loss: 2.3295
[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2290 - loss: 2.3288
[1m128/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2297 - loss: 2.3281
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2301 - loss: 2.3276 - val_accuracy: 0.3415 - val_loss: 2.1725
Epoch 17/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2109 - loss: 2.3339
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2217 - loss: 2.3284 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2289 - loss: 2.3193
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[1m 97/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2318 - loss: 2.3186
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[1m143/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.2336 - loss: 2.3179
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Epoch 18/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2344 - loss: 2.3949
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2328 - loss: 2.3132 
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[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2389 - loss: 2.3012
[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2401 - loss: 2.2992
[1m129/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2408 - loss: 2.2978
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2410 - loss: 2.2974 - val_accuracy: 0.3431 - val_loss: 2.1435
Epoch 19/124

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[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2517 - loss: 2.2503 
[1m 48/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2521 - loss: 2.2548
[1m 74/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2511 - loss: 2.2618
[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2506 - loss: 2.2651
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2504 - loss: 2.2675
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2502 - loss: 2.2688 - val_accuracy: 0.3423 - val_loss: 2.1498
Epoch 20/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2344 - loss: 2.2420
[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2500 - loss: 2.2803 
[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2523 - loss: 2.2713
[1m 71/145[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2532 - loss: 2.2663
[1m 96/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2532 - loss: 2.2635
[1m117/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2529 - loss: 2.2621
[1m139/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.2526 - loss: 2.2611
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2525 - loss: 2.2607 - val_accuracy: 0.3482 - val_loss: 2.1383
Epoch 21/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.1719 - loss: 2.3725
[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2425 - loss: 2.2661 
[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2488 - loss: 2.2595
[1m 73/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2509 - loss: 2.2567
[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2522 - loss: 2.2547
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2527 - loss: 2.2531
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2533 - loss: 2.2516 - val_accuracy: 0.3516 - val_loss: 2.1244
Epoch 22/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2578 - loss: 2.2654
[1m 23/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2663 - loss: 2.2261 
[1m 48/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2681 - loss: 2.2261
[1m 73/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2661 - loss: 2.2285
[1m 96/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2651 - loss: 2.2299
[1m120/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2646 - loss: 2.2299
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.2644 - loss: 2.2301
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2644 - loss: 2.2301 - val_accuracy: 0.3587 - val_loss: 2.1302
Epoch 23/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 27ms/step - accuracy: 0.2734 - loss: 2.2679
[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2669 - loss: 2.2073 
[1m 48/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2680 - loss: 2.2111
[1m 74/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2687 - loss: 2.2099
[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2686 - loss: 2.2088
[1m122/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2691 - loss: 2.2074
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2691 - loss: 2.2067 - val_accuracy: 0.3639 - val_loss: 2.1149
Epoch 24/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.3125 - loss: 2.1597
[1m 28/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2921 - loss: 2.1870 
[1m 55/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2814 - loss: 2.1966
[1m 79/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2772 - loss: 2.2013
[1m106/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2753 - loss: 2.2011
[1m130/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2742 - loss: 2.2005
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2737 - loss: 2.2004 - val_accuracy: 0.3490 - val_loss: 2.1027
Epoch 25/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 25ms/step - accuracy: 0.2734 - loss: 2.1212
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2557 - loss: 2.2198 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2633 - loss: 2.2117
[1m 75/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2653 - loss: 2.2064
[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2669 - loss: 2.2018
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2678 - loss: 2.1993
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2683 - loss: 2.1975 - val_accuracy: 0.3681 - val_loss: 2.0968
Epoch 26/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.2422 - loss: 2.1618
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2724 - loss: 2.1765 
[1m 54/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2746 - loss: 2.1723
[1m 79/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2753 - loss: 2.1734
[1m105/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2760 - loss: 2.1734
[1m131/145[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.2762 - loss: 2.1732
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2762 - loss: 2.1729 - val_accuracy: 0.3667 - val_loss: 2.0961
Epoch 27/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2969 - loss: 2.0663
[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2839 - loss: 2.1638 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2793 - loss: 2.1779
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2792 - loss: 2.1765
[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2801 - loss: 2.1724
[1m128/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2805 - loss: 2.1694
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2807 - loss: 2.1676 - val_accuracy: 0.3619 - val_loss: 2.0817
Epoch 28/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.2656 - loss: 2.1276
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2781 - loss: 2.1306 
[1m 53/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2785 - loss: 2.1353
[1m 79/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2790 - loss: 2.1392
[1m104/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2794 - loss: 2.1411
[1m129/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2796 - loss: 2.1415
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2798 - loss: 2.1417 - val_accuracy: 0.3635 - val_loss: 2.0813
Epoch 29/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.3281 - loss: 2.0093
[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3042 - loss: 2.0754 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2990 - loss: 2.1022
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2969 - loss: 2.1099
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2953 - loss: 2.1139
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2941 - loss: 2.1174
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2935 - loss: 2.1194 - val_accuracy: 0.3776 - val_loss: 2.0757
Epoch 30/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.3047 - loss: 2.1652
[1m 24/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2951 - loss: 2.1244 
[1m 48/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2931 - loss: 2.1218
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[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2906 - loss: 2.1232
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Epoch 31/124

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[1m 24/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3054 - loss: 2.0792 
[1m 47/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3016 - loss: 2.0909
[1m 72/145[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2990 - loss: 2.0964
[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2973 - loss: 2.0998
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Epoch 32/124

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[1m 23/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3040 - loss: 2.0780 
[1m 48/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3005 - loss: 2.0864
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[1m 94/145[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2985 - loss: 2.0979
[1m119/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2982 - loss: 2.0990
[1m143/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.2980 - loss: 2.0991
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Epoch 33/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3047 - loss: 2.0786 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3041 - loss: 2.0796
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3032 - loss: 2.0836
[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3027 - loss: 2.0852
[1m126/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3024 - loss: 2.0854
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3021 - loss: 2.0853 - val_accuracy: 0.3748 - val_loss: 2.0431
Epoch 34/124

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[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3038 - loss: 2.0698 
[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3033 - loss: 2.0727
[1m 73/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3032 - loss: 2.0727
[1m 97/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3033 - loss: 2.0726
[1m121/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3037 - loss: 2.0719
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3044 - loss: 2.0713 - val_accuracy: 0.3846 - val_loss: 2.0309
Epoch 35/124

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[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3307 - loss: 2.0439 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3226 - loss: 2.0521
[1m 73/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3184 - loss: 2.0548
[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3157 - loss: 2.0565
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3148 - loss: 2.0555
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3141 - loss: 2.0549 - val_accuracy: 0.3871 - val_loss: 2.0409
Epoch 36/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2656 - loss: 2.1407
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3114 - loss: 2.0537 
[1m 53/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3113 - loss: 2.0576
[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3116 - loss: 2.0582
[1m104/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3123 - loss: 2.0580
[1m128/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3124 - loss: 2.0575
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3123 - loss: 2.0572 - val_accuracy: 0.3865 - val_loss: 2.0276
Epoch 37/124

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Epoch 38/124

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Epoch 39/124

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Epoch 40/124

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Epoch 41/124

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Epoch 42/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3047 - loss: 2.0343 
[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3088 - loss: 2.0257
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Epoch 43/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3291 - loss: 1.9786 
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[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3336 - loss: 1.9823 - val_accuracy: 0.3994 - val_loss: 2.0030
Epoch 44/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.3828 - loss: 1.8511
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[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3372 - loss: 1.9690
[1m126/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3370 - loss: 1.9720
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3370 - loss: 1.9730 - val_accuracy: 0.3981 - val_loss: 1.9936
Epoch 45/124

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[1m104/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3363 - loss: 1.9776
[1m128/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3369 - loss: 1.9761
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3369 - loss: 1.9758 - val_accuracy: 0.4110 - val_loss: 1.9765
Epoch 46/124

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[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3458 - loss: 1.9477
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[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3447 - loss: 1.9489 - val_accuracy: 0.4086 - val_loss: 1.9973
Epoch 47/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.3594 - loss: 2.0170
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[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3399 - loss: 1.9727
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[1m105/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3411 - loss: 1.9651
[1m131/145[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.3411 - loss: 1.9632
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3412 - loss: 1.9620 - val_accuracy: 0.3907 - val_loss: 1.9708

[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:14[0m 878ms/step
[1m56/86[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 917us/step  
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step 
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 11ms/step
Saved model to disk.

Accuracy capturado en la ejecución 9: 33.69 [%]
F1-score capturado en la ejecución 9: 32.97 [%]

=== EJECUCIÓN 10 ===

--- TRAIN (ejecución 10) ---

--- TEST (ejecución 10) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 409,231 (1.56 MB)
 Trainable params: 409,231 (1.56 MB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 6, 250)
(18469, 6, 250)

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[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 15ms/step
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[1m 59/158[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 869us/step
[1m121/158[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 840us/step
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 39.07 [%]
Global F1 score (validation) = 37.64 [%]
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[[0.00166083 0.0007297  0.0016985  ... 0.035246   0.00280093 0.00080844]
 [0.00190339 0.00105336 0.00204046 ... 0.08087368 0.00304788 0.00078754]
 [0.00165274 0.00051125 0.00166003 ... 0.06369504 0.00235067 0.00102365]
 ...
 [0.17950065 0.04648092 0.2209617  ... 0.00083103 0.2282204  0.08122431]
 [0.13691644 0.04949743 0.24243355 ... 0.00070291 0.18907881 0.12251756]
 [0.18067989 0.04665227 0.2084217  ... 0.0008475  0.23329827 0.08459271]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 41.59 [%]
Global accuracy score (test) = 32.59 [%]
Global F1 score (train) = 41.61 [%]
Global F1 score (test) = 32.43 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.33      0.39      0.36       184
 CAMINAR CON MÓVIL O LIBRO       0.38      0.34      0.36       184
       CAMINAR USUAL SPEED       0.22      0.11      0.15       184
            CAMINAR ZIGZAG       0.04      0.03      0.03       184
          DE PIE BARRIENDO       0.24      0.12      0.16       184
   DE PIE DOBLANDO TOALLAS       0.35      0.36      0.36       184
    DE PIE MOVIENDO LIBROS       0.38      0.24      0.30       184
          DE PIE USANDO PC       0.25      0.28      0.26       184
        FASE REPOSO CON K5       0.43      0.74      0.55       184
INCREMENTAL CICLOERGOMETRO       0.68      0.60      0.64       184
           SENTADO LEYENDO       0.40      0.31      0.35       184
         SENTADO USANDO PC       0.13      0.14      0.14       184
      SENTADO VIENDO LA TV       0.38      0.39      0.38       184
   SUBIR Y BAJAR ESCALERAS       0.15      0.28      0.19       184
                    TROTAR       0.72      0.57      0.64       161

                  accuracy                           0.33      2737
                 macro avg       0.34      0.33      0.32      2737
              weighted avg       0.34      0.33      0.32      2737

2025-10-28 12:53:26.330476: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-28 12:53:26.341905: 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 00:00:1761652406.355440 1812952 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761652406.359630 1812952 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761652406.369874 1812952 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761652406.369900 1812952 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761652406.369903 1812952 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761652406.369906 1812952 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-28 12:53:26.373235: 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 00:00:1761652408.708364 1812952 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13749 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 409,231 (1.56 MB)
 Trainable params: 409,231 (1.56 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/124
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761652411.235289 1813060 service.cc:152] XLA service 0x7c18a00148a0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761652411.235337 1813060 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-28 12:53:31.288556: 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 00:00:1761652411.579822 1813060 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761652415.288451 1813060 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13:58[0m 6s/step - accuracy: 0.0703 - loss: 3.1697
[1m 24/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.0732 - loss: 3.1992  
[1m 48/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.0745 - loss: 3.1861
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[1m 97/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.0763 - loss: 3.1651
[1m120/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.0776 - loss: 3.1561
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 27ms/step - accuracy: 0.0790 - loss: 3.14652025-10-28 12:53:40.925921: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_316', 12 bytes spill stores, 12 bytes spill loads


[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m12s[0m 43ms/step - accuracy: 0.0790 - loss: 3.1462 - val_accuracy: 0.1914 - val_loss: 2.5014
Epoch 2/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.0944 - loss: 2.9818 
[1m 53/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.0977 - loss: 2.9787
[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.0982 - loss: 2.9797
[1m104/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.0987 - loss: 2.9783
[1m128/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.0992 - loss: 2.9764
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.0995 - loss: 2.9747 - val_accuracy: 0.1930 - val_loss: 2.4425
Epoch 3/124

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Epoch 4/124

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Epoch 5/124

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Epoch 6/124

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[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1555 - loss: 2.6949
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Epoch 7/124

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[1m 97/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1612 - loss: 2.6290
[1m122/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1613 - loss: 2.6285
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.1613 - loss: 2.6275 - val_accuracy: 0.2255 - val_loss: 2.3110
Epoch 8/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1664 - loss: 2.5866 
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[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1711 - loss: 2.5883
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1711 - loss: 2.5880
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.1712 - loss: 2.5871 - val_accuracy: 0.2458 - val_loss: 2.3063
Epoch 9/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.1484 - loss: 2.6283
[1m 28/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1658 - loss: 2.5873 
[1m 54/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1679 - loss: 2.5766
[1m 81/145[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1699 - loss: 2.5704
[1m105/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1709 - loss: 2.5669
[1m130/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1715 - loss: 2.5641
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.1719 - loss: 2.5626 - val_accuracy: 0.2644 - val_loss: 2.2795
Epoch 10/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2007 - loss: 2.4716 
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[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1949 - loss: 2.4963
[1m123/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1938 - loss: 2.4985
[1m144/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.1928 - loss: 2.4998
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.1928 - loss: 2.4999 - val_accuracy: 0.2762 - val_loss: 2.2765
Epoch 11/124

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[1m 24/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1853 - loss: 2.5320 
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[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1905 - loss: 2.5044
[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1901 - loss: 2.5004
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Epoch 12/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2077 - loss: 2.4268 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2076 - loss: 2.4290
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[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2068 - loss: 2.4334
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[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2060 - loss: 2.4358 - val_accuracy: 0.3085 - val_loss: 2.2463
Epoch 13/124

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[1m 24/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1924 - loss: 2.4697 
[1m 48/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1977 - loss: 2.4584
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[1m 92/145[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2000 - loss: 2.4487
[1m118/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2012 - loss: 2.4448
[1m141/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.2024 - loss: 2.4416
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2026 - loss: 2.4410 - val_accuracy: 0.3190 - val_loss: 2.2328
Epoch 14/124

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[1m 28/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2156 - loss: 2.4302 
[1m 53/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2153 - loss: 2.4231
[1m 79/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2146 - loss: 2.4190
[1m104/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2145 - loss: 2.4155
[1m129/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2142 - loss: 2.4133
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2140 - loss: 2.4123 - val_accuracy: 0.3210 - val_loss: 2.2183
Epoch 15/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2050 - loss: 2.4038 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2140 - loss: 2.3866
[1m 79/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2156 - loss: 2.3844
[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2164 - loss: 2.3829
[1m127/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2173 - loss: 2.3808
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2179 - loss: 2.3797 - val_accuracy: 0.3367 - val_loss: 2.1986
Epoch 16/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2315 - loss: 2.3532 
[1m 53/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2273 - loss: 2.3535
[1m 80/145[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2260 - loss: 2.3536
[1m104/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2249 - loss: 2.3549
[1m130/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2240 - loss: 2.3553
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2238 - loss: 2.3554 - val_accuracy: 0.3371 - val_loss: 2.1870
Epoch 17/124

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[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2330 - loss: 2.3229 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2273 - loss: 2.3344
[1m 74/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2268 - loss: 2.3365
[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2274 - loss: 2.3370
[1m122/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2286 - loss: 2.3363
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2292 - loss: 2.3357 - val_accuracy: 0.3470 - val_loss: 2.1596
Epoch 18/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2301 - loss: 2.2955 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2337 - loss: 2.3003
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2357 - loss: 2.3028
[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2364 - loss: 2.3043
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Epoch 19/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2403 - loss: 2.3132 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2397 - loss: 2.3139
[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2413 - loss: 2.3091
[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2413 - loss: 2.3061
[1m126/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2414 - loss: 2.3034
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2415 - loss: 2.3019 - val_accuracy: 0.3552 - val_loss: 2.1493
Epoch 20/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2381 - loss: 2.3164 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2400 - loss: 2.3058
[1m 74/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2414 - loss: 2.2985
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2430 - loss: 2.2932
[1m122/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2441 - loss: 2.2897
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2448 - loss: 2.2870 - val_accuracy: 0.3538 - val_loss: 2.1395
Epoch 21/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2443 - loss: 2.2455 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2448 - loss: 2.2583
[1m 74/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2438 - loss: 2.2646
[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2443 - loss: 2.2659
[1m121/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2453 - loss: 2.2656
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2465 - loss: 2.2645 - val_accuracy: 0.3403 - val_loss: 2.1668
Epoch 22/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2568 - loss: 2.2359 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2576 - loss: 2.2434
[1m 79/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2595 - loss: 2.2412
[1m104/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2606 - loss: 2.2399
[1m129/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2611 - loss: 2.2395
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2611 - loss: 2.2395 - val_accuracy: 0.3514 - val_loss: 2.1447
Epoch 23/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.2578 - loss: 2.2082
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2620 - loss: 2.2244 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2632 - loss: 2.2254
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2628 - loss: 2.2293
[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2620 - loss: 2.2314
[1m127/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2614 - loss: 2.2324
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2613 - loss: 2.2323 - val_accuracy: 0.3607 - val_loss: 2.1390
Epoch 24/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2812 - loss: 2.1294
[1m 28/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2735 - loss: 2.1837 
[1m 53/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2709 - loss: 2.1948
[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2684 - loss: 2.2027
[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2676 - loss: 2.2066
[1m127/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2673 - loss: 2.2083
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2674 - loss: 2.2088 - val_accuracy: 0.3697 - val_loss: 2.1234
Epoch 25/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2803 - loss: 2.1609 
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[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2770 - loss: 2.1700
[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2768 - loss: 2.1720
[1m129/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2762 - loss: 2.1748
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2756 - loss: 2.1768 - val_accuracy: 0.3603 - val_loss: 2.1202
Epoch 26/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.2812 - loss: 2.1109
[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2783 - loss: 2.1645 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2749 - loss: 2.1779
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[1m 97/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2750 - loss: 2.1787
[1m123/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2752 - loss: 2.1787
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2751 - loss: 2.1792 - val_accuracy: 0.3661 - val_loss: 2.0975
Epoch 27/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2578 - loss: 2.0912
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2578 - loss: 2.1956 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2601 - loss: 2.1950
[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2634 - loss: 2.1912
[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2658 - loss: 2.1872
[1m128/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2674 - loss: 2.1842
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2686 - loss: 2.1822 - val_accuracy: 0.3554 - val_loss: 2.1145
Epoch 28/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2656 - loss: 2.0994
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2677 - loss: 2.1587 
[1m 53/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2715 - loss: 2.1635
[1m 79/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2736 - loss: 2.1651
[1m105/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2755 - loss: 2.1641
[1m129/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2770 - loss: 2.1622
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2777 - loss: 2.1612 - val_accuracy: 0.3617 - val_loss: 2.1046
Epoch 29/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 25ms/step - accuracy: 0.2422 - loss: 2.1938
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2848 - loss: 2.1519 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2869 - loss: 2.1525
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2873 - loss: 2.1521
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2869 - loss: 2.1523
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2871 - loss: 2.1509
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2872 - loss: 2.1500 - val_accuracy: 0.3629 - val_loss: 2.0815
Epoch 30/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2578 - loss: 2.0887
[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2842 - loss: 2.1170 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2837 - loss: 2.1212
[1m 74/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2837 - loss: 2.1250
[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2847 - loss: 2.1262
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2851 - loss: 2.1271
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2854 - loss: 2.1274 - val_accuracy: 0.3808 - val_loss: 2.0581
Epoch 31/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2344 - loss: 2.1679
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2875 - loss: 2.1371 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2892 - loss: 2.1359
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2897 - loss: 2.1352
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2900 - loss: 2.1352
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2899 - loss: 2.1348
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2901 - loss: 2.1339 - val_accuracy: 0.3716 - val_loss: 2.0659
Epoch 32/124

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Epoch 33/124

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Epoch 34/124

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Epoch 35/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2891 - loss: 2.0979 
[1m 54/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2931 - loss: 2.0951
[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2947 - loss: 2.0923
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2958 - loss: 2.0905
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Epoch 36/124

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[1m 24/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3077 - loss: 2.0633 
[1m 48/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3064 - loss: 2.0654
[1m 73/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3071 - loss: 2.0652
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3080 - loss: 2.0641
[1m127/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3088 - loss: 2.0627
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3093 - loss: 2.0621 - val_accuracy: 0.3738 - val_loss: 2.0457
Epoch 37/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3026 - loss: 2.0669 
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[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3069 - loss: 2.0623
[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3071 - loss: 2.0604
[1m130/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3075 - loss: 2.0589
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3077 - loss: 2.0582 - val_accuracy: 0.3808 - val_loss: 2.0357
Epoch 38/124

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[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3038 - loss: 2.0662
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[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3049 - loss: 2.0635
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[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3069 - loss: 2.0591 - val_accuracy: 0.3840 - val_loss: 2.0477
Epoch 39/124

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Epoch 40/124

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[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3199 - loss: 2.0131
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Epoch 41/124

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Epoch 42/124

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[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3176 - loss: 2.0115
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[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3208 - loss: 2.0104
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Epoch 43/124

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[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3242 - loss: 1.9771 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3251 - loss: 1.9815
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3250 - loss: 1.9863
[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3249 - loss: 1.9891
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3247 - loss: 1.9909
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3245 - loss: 1.9926 - val_accuracy: 0.3861 - val_loss: 2.0174
Epoch 44/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3227 - loss: 1.9972 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3250 - loss: 1.9964
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3263 - loss: 1.9953
[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3276 - loss: 1.9944
[1m127/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3286 - loss: 1.9933
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3288 - loss: 1.9932 - val_accuracy: 0.3913 - val_loss: 2.0083
Epoch 45/124

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[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3404 - loss: 1.9603
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Epoch 46/124

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[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3253 - loss: 1.9820
[1m 73/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3277 - loss: 1.9780
[1m 96/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3283 - loss: 1.9772
[1m116/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3284 - loss: 1.9777
[1m140/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.3285 - loss: 1.9776
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Epoch 47/124

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[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3344 - loss: 1.9776
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[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3351 - loss: 1.9708
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Epoch 48/124

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[1m 23/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3403 - loss: 1.9498 
[1m 47/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3432 - loss: 1.9469
[1m 72/145[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3455 - loss: 1.9441
[1m 97/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3455 - loss: 1.9454
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Epoch 49/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3375 - loss: 1.9584 
[1m 54/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3388 - loss: 1.9537
[1m 79/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3391 - loss: 1.9508
[1m104/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3401 - loss: 1.9471
[1m130/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3413 - loss: 1.9448
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3419 - loss: 1.9440 - val_accuracy: 0.4106 - val_loss: 2.0046
Epoch 50/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3442 - loss: 1.9495 
[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3436 - loss: 1.9458
[1m 75/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3421 - loss: 1.9466
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3420 - loss: 1.9445
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3424 - loss: 1.9425
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3426 - loss: 1.9416 - val_accuracy: 0.4044 - val_loss: 1.9836
Epoch 51/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.3203 - loss: 2.0210
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3550 - loss: 1.9319 
[1m 53/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3531 - loss: 1.9251
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3513 - loss: 1.9255
[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3502 - loss: 1.9258
[1m126/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3495 - loss: 1.9259
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3490 - loss: 1.9263 - val_accuracy: 0.4014 - val_loss: 1.9962
Epoch 52/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.3984 - loss: 1.7933
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3648 - loss: 1.9028 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3574 - loss: 1.9193
[1m 74/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3543 - loss: 1.9256
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3525 - loss: 1.9280
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3519 - loss: 1.9285
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3518 - loss: 1.9282 - val_accuracy: 0.3986 - val_loss: 1.9881
Epoch 53/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.3906 - loss: 2.0156
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3611 - loss: 1.9281 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3589 - loss: 1.9233
[1m 74/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3576 - loss: 1.9224
[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3574 - loss: 1.9205
[1m122/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3573 - loss: 1.9192
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3572 - loss: 1.9181 - val_accuracy: 0.4040 - val_loss: 1.9987
Epoch 54/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.3438 - loss: 1.9513
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3421 - loss: 1.9328 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3454 - loss: 1.9227
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3469 - loss: 1.9181
[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3471 - loss: 1.9175
[1m122/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3476 - loss: 1.9162
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3482 - loss: 1.9147 - val_accuracy: 0.3996 - val_loss: 2.0037
Epoch 55/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.4375 - loss: 1.8384
[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3806 - loss: 1.8653 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3733 - loss: 1.8812
[1m 79/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3703 - loss: 1.8879
[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3688 - loss: 1.8904
[1m129/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3680 - loss: 1.8919
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3678 - loss: 1.8928 - val_accuracy: 0.4016 - val_loss: 2.0020

[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:15[0m 884ms/step
[1m58/86[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 880us/step  
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step 
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 11ms/step
Saved model to disk.

Accuracy capturado en la ejecución 10: 32.59 [%]
F1-score capturado en la ejecución 10: 32.43 [%]

=== EJECUCIÓN 11 ===

--- TRAIN (ejecución 11) ---

--- TEST (ejecución 11) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 409,231 (1.56 MB)
 Trainable params: 409,231 (1.56 MB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 6, 250)
(18469, 6, 250)

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m10:19[0m 1s/step
[1m 63/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 811us/step
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[1m193/578[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 786us/step
[1m256/578[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 789us/step
[1m321/578[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 785us/step
[1m381/578[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 794us/step
[1m444/578[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 795us/step
[1m509/578[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 793us/step
[1m564/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 806us/step
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 2ms/step

[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 17ms/step
[1m59/86[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 869us/step
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step 
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 10ms/step

[1m  1/158[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step
[1m 55/158[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 927us/step
[1m110/158[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 924us/step
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 40.16 [%]
Global F1 score (validation) = 38.7 [%]
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[[0.00158392 0.0008747  0.00166846 ... 0.03812157 0.00216539 0.00060954]
 [0.00129431 0.00089932 0.00195834 ... 0.08655623 0.00227452 0.00059806]
 [0.00255403 0.00085096 0.00260943 ... 0.05363473 0.00309172 0.00121482]
 ...
 [0.16399494 0.03958351 0.22809437 ... 0.00041017 0.23865862 0.08429468]
 [0.16163129 0.07886022 0.21621358 ... 0.00083983 0.18575391 0.09350844]
 [0.19348109 0.04294482 0.20529892 ... 0.00051575 0.25202504 0.07112138]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 43.87 [%]
Global accuracy score (test) = 34.97 [%]
Global F1 score (train) = 43.79 [%]
Global F1 score (test) = 34.43 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.31      0.46      0.37       184
 CAMINAR CON MÓVIL O LIBRO       0.36      0.32      0.34       184
       CAMINAR USUAL SPEED       0.19      0.14      0.16       184
            CAMINAR ZIGZAG       0.12      0.14      0.13       184
          DE PIE BARRIENDO       0.30      0.23      0.26       184
   DE PIE DOBLANDO TOALLAS       0.34      0.31      0.33       184
    DE PIE MOVIENDO LIBROS       0.34      0.19      0.24       184
          DE PIE USANDO PC       0.27      0.21      0.23       184
        FASE REPOSO CON K5       0.48      0.74      0.59       184
INCREMENTAL CICLOERGOMETRO       0.53      0.61      0.57       184
           SENTADO LEYENDO       0.52      0.47      0.49       184
         SENTADO USANDO PC       0.21      0.20      0.20       184
      SENTADO VIENDO LA TV       0.43      0.52      0.47       184
   SUBIR Y BAJAR ESCALERAS       0.17      0.22      0.19       184
                    TROTAR       0.68      0.52      0.59       161

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

2025-10-28 12:54:17.992305: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-28 12:54:18.003678: 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 00:00:1761652458.017374 1819108 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761652458.021647 1819108 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761652458.031733 1819108 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761652458.031759 1819108 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761652458.031762 1819108 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761652458.031764 1819108 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-28 12:54:18.035066: 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 00:00:1761652460.372377 1819108 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13749 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 409,231 (1.56 MB)
 Trainable params: 409,231 (1.56 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/124
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761652462.894141 1819228 service.cc:152] XLA service 0x76477c004b80 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761652462.894212 1819228 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-28 12:54:22.954464: 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 00:00:1761652463.245814 1819228 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761652466.909761 1819228 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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
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Epoch 2/124

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Epoch 3/124

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Epoch 4/124

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Epoch 5/124

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Epoch 6/124

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Epoch 7/124

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Epoch 8/124

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[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1754 - loss: 2.5991
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1755 - loss: 2.5954
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Epoch 9/124

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[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1755 - loss: 2.5454
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1762 - loss: 2.5430
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.1767 - loss: 2.5413 - val_accuracy: 0.2775 - val_loss: 2.2628
Epoch 10/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1833 - loss: 2.5008 
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[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1868 - loss: 2.5005
[1m104/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1872 - loss: 2.5000
[1m129/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1873 - loss: 2.4997
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.1872 - loss: 2.4994 - val_accuracy: 0.2962 - val_loss: 2.2669
Epoch 11/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1973 - loss: 2.4881 
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[1m 79/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1979 - loss: 2.4778
[1m107/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1983 - loss: 2.4754
[1m133/145[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.1984 - loss: 2.4738
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Epoch 12/124

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Epoch 13/124

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Epoch 14/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2084 - loss: 2.3990 
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Epoch 15/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2177 - loss: 2.3565 
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[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2152 - loss: 2.3658
[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2160 - loss: 2.3669
[1m129/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2166 - loss: 2.3670
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Epoch 16/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2189 - loss: 2.3563 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2204 - loss: 2.3531
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2194 - loss: 2.3552
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2196 - loss: 2.3560
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2200 - loss: 2.3553
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2207 - loss: 2.3538 - val_accuracy: 0.3399 - val_loss: 2.1765
Epoch 17/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2192 - loss: 2.3584 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2209 - loss: 2.3527
[1m 75/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2230 - loss: 2.3474
[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2243 - loss: 2.3430
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2251 - loss: 2.3400
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2257 - loss: 2.3380 - val_accuracy: 0.3423 - val_loss: 2.1893
Epoch 18/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.2734 - loss: 2.2691
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2402 - loss: 2.3073 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2375 - loss: 2.3141
[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2358 - loss: 2.3145
[1m104/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2350 - loss: 2.3147
[1m128/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2346 - loss: 2.3141
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2345 - loss: 2.3133 - val_accuracy: 0.3617 - val_loss: 2.1604
Epoch 19/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2421 - loss: 2.2993 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2435 - loss: 2.2930
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2440 - loss: 2.2920
[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2439 - loss: 2.2927
[1m129/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2439 - loss: 2.2926
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2440 - loss: 2.2925 - val_accuracy: 0.3691 - val_loss: 2.1620
Epoch 20/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2109 - loss: 2.3892
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2356 - loss: 2.2795 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2416 - loss: 2.2755
[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2438 - loss: 2.2752
[1m104/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2458 - loss: 2.2736
[1m127/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2469 - loss: 2.2723
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2474 - loss: 2.2718 - val_accuracy: 0.3574 - val_loss: 2.1528
Epoch 21/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2500 - loss: 2.2534
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2514 - loss: 2.2529 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2520 - loss: 2.2499
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2536 - loss: 2.2493
[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2540 - loss: 2.2496
[1m123/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2538 - loss: 2.2501
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2536 - loss: 2.2507 - val_accuracy: 0.3695 - val_loss: 2.1301
Epoch 22/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2500 - loss: 2.3381
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2592 - loss: 2.2771 
[1m 53/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2565 - loss: 2.2635
[1m 79/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2573 - loss: 2.2557
[1m104/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2582 - loss: 2.2514
[1m128/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2587 - loss: 2.2486
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2589 - loss: 2.2472 - val_accuracy: 0.3613 - val_loss: 2.1188
Epoch 23/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.1797 - loss: 2.2941
[1m 24/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2507 - loss: 2.2261 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2542 - loss: 2.2285
[1m 75/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2548 - loss: 2.2298
[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2563 - loss: 2.2295
[1m126/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2573 - loss: 2.2280
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2580 - loss: 2.2266 - val_accuracy: 0.3661 - val_loss: 2.1319
Epoch 24/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.2422 - loss: 2.2998
[1m 29/145[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2728 - loss: 2.2446 
[1m 56/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2743 - loss: 2.2313
[1m 81/145[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2737 - loss: 2.2276
[1m105/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2733 - loss: 2.2249
[1m130/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2732 - loss: 2.2224
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2731 - loss: 2.2209 - val_accuracy: 0.3728 - val_loss: 2.1117
Epoch 25/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.2734 - loss: 2.2186
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2678 - loss: 2.1757 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2682 - loss: 2.1852
[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2685 - loss: 2.1860
[1m104/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2687 - loss: 2.1861
[1m130/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2691 - loss: 2.1860
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2694 - loss: 2.1860 - val_accuracy: 0.3580 - val_loss: 2.1160
Epoch 26/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.1953 - loss: 2.2622
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2718 - loss: 2.1684 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2756 - loss: 2.1707
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[1m104/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2763 - loss: 2.1719
[1m130/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2758 - loss: 2.1739
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Epoch 27/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2890 - loss: 2.1613 
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[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2835 - loss: 2.1604
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Epoch 28/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2904 - loss: 2.1497 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2898 - loss: 2.1515
[1m 74/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2891 - loss: 2.1534
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2886 - loss: 2.1552
[1m126/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2880 - loss: 2.1562
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Epoch 29/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 33ms/step - accuracy: 0.2422 - loss: 2.2873
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2713 - loss: 2.1800 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2798 - loss: 2.1541
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2826 - loss: 2.1477
[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2835 - loss: 2.1461
[1m123/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2842 - loss: 2.1451
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2842 - loss: 2.1450 - val_accuracy: 0.3736 - val_loss: 2.0655
Epoch 30/124

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[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2747 - loss: 2.1292 
[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2813 - loss: 2.1311
[1m 75/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2839 - loss: 2.1307
[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2849 - loss: 2.1288
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2855 - loss: 2.1281
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2858 - loss: 2.1275 - val_accuracy: 0.3726 - val_loss: 2.0631
Epoch 31/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.2969 - loss: 2.0818
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2820 - loss: 2.1214 
[1m 53/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2864 - loss: 2.1215
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2883 - loss: 2.1205
[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2897 - loss: 2.1197
[1m128/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2904 - loss: 2.1191
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2908 - loss: 2.1186 - val_accuracy: 0.3661 - val_loss: 2.0877
Epoch 32/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2422 - loss: 2.1905
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3084 - loss: 2.0881 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3066 - loss: 2.0941
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3033 - loss: 2.0970
[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3015 - loss: 2.0987
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3005 - loss: 2.1003
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3003 - loss: 2.1008 - val_accuracy: 0.3756 - val_loss: 2.0724
Epoch 33/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2734 - loss: 2.1387
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2826 - loss: 2.1088 
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Epoch 34/124

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Epoch 35/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3002 - loss: 2.1032 
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[1m107/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3051 - loss: 2.0845
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Epoch 36/124

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[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3033 - loss: 2.1086 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3091 - loss: 2.0849
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[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3130 - loss: 2.0701
[1m126/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3129 - loss: 2.0678
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Epoch 37/124

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[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3235 - loss: 2.0506 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3182 - loss: 2.0511
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3160 - loss: 2.0533
[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3143 - loss: 2.0556
[1m128/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3136 - loss: 2.0563
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3133 - loss: 2.0562 - val_accuracy: 0.3937 - val_loss: 2.0395
Epoch 38/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.3125 - loss: 2.0581
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3087 - loss: 2.0486 
[1m 53/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3109 - loss: 2.0515
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3115 - loss: 2.0508
[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3115 - loss: 2.0484
[1m129/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3116 - loss: 2.0464
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3118 - loss: 2.0452 - val_accuracy: 0.3834 - val_loss: 2.0316
Epoch 39/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2656 - loss: 2.0373
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3073 - loss: 2.0375 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3090 - loss: 2.0328
[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3094 - loss: 2.0323
[1m104/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3109 - loss: 2.0310
[1m129/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3121 - loss: 2.0301
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3124 - loss: 2.0299 - val_accuracy: 0.3913 - val_loss: 2.0154
Epoch 40/124

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Epoch 41/124

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Epoch 42/124

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Epoch 43/124

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[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3169 - loss: 2.0201
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[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3172 - loss: 2.0175
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Epoch 44/124

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[1m 24/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3350 - loss: 2.0076 
[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3357 - loss: 1.9986
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[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3329 - loss: 1.9955
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3326 - loss: 1.9934
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3324 - loss: 1.9924 - val_accuracy: 0.3935 - val_loss: 1.9989
Epoch 45/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3520 - loss: 1.9407 
[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3463 - loss: 1.9516
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[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3423 - loss: 1.9616
[1m126/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3414 - loss: 1.9643
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3407 - loss: 1.9659 - val_accuracy: 0.4036 - val_loss: 1.9887
Epoch 46/124

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[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3366 - loss: 1.9855
[1m126/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3372 - loss: 1.9818
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3372 - loss: 1.9802 - val_accuracy: 0.3981 - val_loss: 2.0002
Epoch 47/124

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[1m 79/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3377 - loss: 1.9527
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Epoch 48/124

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[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3619 - loss: 1.9189
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[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3563 - loss: 1.9260
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Epoch 49/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3382 - loss: 1.9339 
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Epoch 50/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3438 - loss: 1.9026 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3490 - loss: 1.9051
[1m 72/145[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3499 - loss: 1.9064
[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3503 - loss: 1.9089
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3501 - loss: 1.9114
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3497 - loss: 1.9139 - val_accuracy: 0.4026 - val_loss: 1.9603
Epoch 51/124

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[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3684 - loss: 1.8787 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3662 - loss: 1.8896
[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3630 - loss: 1.8981
[1m104/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3610 - loss: 1.9027
[1m130/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3594 - loss: 1.9053
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3588 - loss: 1.9067 - val_accuracy: 0.3992 - val_loss: 2.0015
Epoch 52/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.3750 - loss: 1.9141
[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3643 - loss: 1.8896 
[1m 48/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3642 - loss: 1.8913
[1m 74/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3607 - loss: 1.8989
[1m 96/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3589 - loss: 1.9030
[1m121/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3579 - loss: 1.9059
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3575 - loss: 1.9075
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3575 - loss: 1.9076 - val_accuracy: 0.3990 - val_loss: 1.9714
Epoch 53/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3501 - loss: 1.8969 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3529 - loss: 1.8960
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3553 - loss: 1.8979
[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3572 - loss: 1.8987
[1m127/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3582 - loss: 1.8993
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3587 - loss: 1.8998 - val_accuracy: 0.4066 - val_loss: 1.9665
Epoch 54/124

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Saved model to disk.

Accuracy capturado en la ejecución 11: 34.97 [%]
F1-score capturado en la ejecución 11: 34.43 [%]

=== EJECUCIÓN 12 ===

--- TRAIN (ejecución 12) ---

--- TEST (ejecución 12) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 409,231 (1.56 MB)
 Trainable params: 409,231 (1.56 MB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 6, 250)
(18469, 6, 250)

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[1m247/578[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 820us/step
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[1m51/86[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step 
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 9ms/step

[1m  1/158[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step
[1m 60/158[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 848us/step
[1m125/158[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 812us/step
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 40.04 [%]
Global F1 score (validation) = 38.21 [%]
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[[0.00285256 0.00220843 0.00345676 ... 0.02984024 0.00612653 0.00072708]
 [0.00172538 0.00154866 0.00281699 ... 0.04529419 0.00422745 0.00053323]
 [0.00089224 0.00052912 0.00165095 ... 0.01226    0.00210116 0.00050264]
 ...
 [0.17194206 0.04232787 0.21528919 ... 0.00042251 0.25434682 0.08475454]
 [0.18626463 0.08478867 0.19952476 ... 0.00063375 0.21519302 0.09940718]
 [0.18398403 0.03663015 0.20765321 ... 0.00058102 0.25667763 0.0718158 ]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 42.91 [%]
Global accuracy score (test) = 32.15 [%]
Global F1 score (train) = 42.41 [%]
Global F1 score (test) = 31.61 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.27      0.29      0.28       184
 CAMINAR CON MÓVIL O LIBRO       0.37      0.33      0.35       184
       CAMINAR USUAL SPEED       0.18      0.09      0.12       184
            CAMINAR ZIGZAG       0.09      0.05      0.06       184
          DE PIE BARRIENDO       0.28      0.26      0.27       184
   DE PIE DOBLANDO TOALLAS       0.36      0.33      0.34       184
    DE PIE MOVIENDO LIBROS       0.35      0.27      0.30       184
          DE PIE USANDO PC       0.26      0.21      0.23       184
        FASE REPOSO CON K5       0.44      0.74      0.55       184
INCREMENTAL CICLOERGOMETRO       0.58      0.64      0.61       184
           SENTADO LEYENDO       0.31      0.31      0.31       184
         SENTADO USANDO PC       0.18      0.13      0.15       184
      SENTADO VIENDO LA TV       0.37      0.31      0.34       184
   SUBIR Y BAJAR ESCALERAS       0.14      0.34      0.20       184
                    TROTAR       0.72      0.55      0.63       161

                  accuracy                           0.32      2737
                 macro avg       0.33      0.32      0.32      2737
              weighted avg       0.32      0.32      0.31      2737

2025-10-28 12:55:08.857364: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-28 12:55:08.868892: 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 00:00:1761652508.882233 1825201 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761652508.886353 1825201 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761652508.896527 1825201 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761652508.896550 1825201 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761652508.896554 1825201 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761652508.896556 1825201 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-28 12:55:08.899804: 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 00:00:1761652511.268286 1825201 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13749 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 409,231 (1.56 MB)
 Trainable params: 409,231 (1.56 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/124
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761652513.812667 1825324 service.cc:152] XLA service 0x7b6e80011e30 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761652513.812726 1825324 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-28 12:55:13.867880: 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 00:00:1761652514.172149 1825324 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761652517.837308 1825324 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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
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Epoch 2/124

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Epoch 3/124

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Epoch 4/124

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[1m121/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1394 - loss: 2.7712
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Epoch 5/124

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[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1434 - loss: 2.7192
[1m127/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1442 - loss: 2.7161
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Epoch 6/124

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[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1478 - loss: 2.6587
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Epoch 7/124

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[1m144/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.1648 - loss: 2.6014
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Epoch 8/124

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Epoch 9/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1690 - loss: 2.5579 
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[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1799 - loss: 2.5271
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Epoch 10/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2188 - loss: 2.3955
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1987 - loss: 2.4803 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1968 - loss: 2.4822
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1964 - loss: 2.4809
[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1959 - loss: 2.4804
[1m128/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1957 - loss: 2.4795
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.1957 - loss: 2.4789 - val_accuracy: 0.3010 - val_loss: 2.2290
Epoch 11/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2344 - loss: 2.4447
[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2037 - loss: 2.4630 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2005 - loss: 2.4601
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2009 - loss: 2.4545
[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2009 - loss: 2.4515
[1m127/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2008 - loss: 2.4494
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2007 - loss: 2.4483 - val_accuracy: 0.3192 - val_loss: 2.2124
Epoch 12/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2188 - loss: 2.4119
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2096 - loss: 2.4092 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2090 - loss: 2.4093
[1m 74/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2089 - loss: 2.4103
[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2083 - loss: 2.4123
[1m123/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2078 - loss: 2.4129
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2076 - loss: 2.4135 - val_accuracy: 0.3200 - val_loss: 2.2190
Epoch 13/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.1797 - loss: 2.5196
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2048 - loss: 2.4089 
[1m 53/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2083 - loss: 2.4039
[1m 80/145[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2104 - loss: 2.4006
[1m104/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2120 - loss: 2.3987
[1m128/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2130 - loss: 2.3970
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2135 - loss: 2.3961 - val_accuracy: 0.3411 - val_loss: 2.2028
Epoch 14/124

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[1m 72/145[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2151 - loss: 2.3841
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[1m120/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2161 - loss: 2.3810
[1m144/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.2168 - loss: 2.3790
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Epoch 15/124

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[1m 79/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2271 - loss: 2.3477
[1m105/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2270 - loss: 2.3480
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Epoch 16/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2297 - loss: 2.3177 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2292 - loss: 2.3205
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[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2311 - loss: 2.3204
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2316 - loss: 2.3213
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Epoch 17/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2419 - loss: 2.2921 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2418 - loss: 2.2950
[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2417 - loss: 2.2972
[1m105/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2418 - loss: 2.2978
[1m130/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2415 - loss: 2.2986
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2412 - loss: 2.2990 - val_accuracy: 0.3474 - val_loss: 2.1744
Epoch 18/124

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[1m 24/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2340 - loss: 2.2776 
[1m 48/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2386 - loss: 2.2721
[1m 73/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2404 - loss: 2.2727
[1m 97/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2406 - loss: 2.2751
[1m122/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2401 - loss: 2.2781
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2400 - loss: 2.2802 - val_accuracy: 0.3498 - val_loss: 2.1719
Epoch 19/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 26ms/step - accuracy: 0.2109 - loss: 2.3704
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2397 - loss: 2.2869 
[1m 54/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2420 - loss: 2.2794
[1m 80/145[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2441 - loss: 2.2753
[1m106/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2446 - loss: 2.2746
[1m130/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2451 - loss: 2.2739
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2453 - loss: 2.2737 - val_accuracy: 0.3470 - val_loss: 2.1654
Epoch 20/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.3203 - loss: 2.1605
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2685 - loss: 2.2367 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2676 - loss: 2.2368
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2663 - loss: 2.2385
[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2646 - loss: 2.2411
[1m127/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2631 - loss: 2.2441
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2622 - loss: 2.2457 - val_accuracy: 0.3514 - val_loss: 2.1461
Epoch 21/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2470 - loss: 2.2443 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2516 - loss: 2.2377
[1m 74/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2539 - loss: 2.2362
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2558 - loss: 2.2350
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2569 - loss: 2.2352
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2574 - loss: 2.2353 - val_accuracy: 0.3540 - val_loss: 2.1411
Epoch 22/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2624 - loss: 2.2276 
[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2630 - loss: 2.2180
[1m 74/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2630 - loss: 2.2156
[1m 97/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2630 - loss: 2.2161
[1m121/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2633 - loss: 2.2167
[1m144/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.2636 - loss: 2.2175
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2636 - loss: 2.2175 - val_accuracy: 0.3661 - val_loss: 2.1297
Epoch 23/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2266 - loss: 2.2553
[1m 22/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2483 - loss: 2.2476 
[1m 47/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2579 - loss: 2.2319
[1m 69/145[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2609 - loss: 2.2248
[1m 96/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2627 - loss: 2.2188
[1m122/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2638 - loss: 2.2152
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2641 - loss: 2.2138 - val_accuracy: 0.3695 - val_loss: 2.1034
Epoch 24/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.2500 - loss: 2.1201
[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2736 - loss: 2.1864 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2748 - loss: 2.1916
[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2738 - loss: 2.1939
[1m104/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2727 - loss: 2.1957
[1m128/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2722 - loss: 2.1963
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2721 - loss: 2.1964 - val_accuracy: 0.3722 - val_loss: 2.1152
Epoch 25/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.3281 - loss: 2.1454
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2970 - loss: 2.1662 
[1m 53/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2911 - loss: 2.1687
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2888 - loss: 2.1713
[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2870 - loss: 2.1726
[1m127/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2858 - loss: 2.1727
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2850 - loss: 2.1732 - val_accuracy: 0.3730 - val_loss: 2.0834
Epoch 26/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2656 - loss: 2.2753
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2779 - loss: 2.1862 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2781 - loss: 2.1765
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2774 - loss: 2.1745
[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2779 - loss: 2.1726
[1m128/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2780 - loss: 2.1711
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2779 - loss: 2.1707 - val_accuracy: 0.3709 - val_loss: 2.0895
Epoch 27/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2500 - loss: 2.2629
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2886 - loss: 2.1529 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2906 - loss: 2.1489
[1m 75/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2904 - loss: 2.1514
[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2899 - loss: 2.1531
[1m126/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2896 - loss: 2.1532
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2895 - loss: 2.1530 - val_accuracy: 0.3645 - val_loss: 2.0923
Epoch 28/124

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[1m 48/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2933 - loss: 2.1284
[1m 72/145[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2925 - loss: 2.1307
[1m 97/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2916 - loss: 2.1329
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Epoch 29/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2818 - loss: 2.1554 
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[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2864 - loss: 2.1402
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[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2855 - loss: 2.1388 - val_accuracy: 0.3711 - val_loss: 2.0753
Epoch 30/124

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[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2988 - loss: 2.1280 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2983 - loss: 2.1194
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2970 - loss: 2.1187
[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2959 - loss: 2.1187
[1m127/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2952 - loss: 2.1193
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2949 - loss: 2.1194 - val_accuracy: 0.3716 - val_loss: 2.1162
Epoch 31/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2875 - loss: 2.1164 
[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2938 - loss: 2.1055
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[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2963 - loss: 2.1046
[1m121/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2967 - loss: 2.1050
[1m142/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.2971 - loss: 2.1051
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2972 - loss: 2.1050 - val_accuracy: 0.3754 - val_loss: 2.0544
Epoch 32/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.3594 - loss: 2.0952
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3039 - loss: 2.1124 
[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2995 - loss: 2.1188
[1m 75/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2992 - loss: 2.1192
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2989 - loss: 2.1185
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2991 - loss: 2.1159
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2994 - loss: 2.1141 - val_accuracy: 0.3689 - val_loss: 2.0826
Epoch 33/124

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[1m 24/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2853 - loss: 2.1384 
[1m 47/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2919 - loss: 2.1156
[1m 72/145[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2944 - loss: 2.1069
[1m 97/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2957 - loss: 2.1024
[1m121/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2966 - loss: 2.1003
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2974 - loss: 2.0982 - val_accuracy: 0.3748 - val_loss: 2.0630
Epoch 34/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3075 - loss: 2.0721 
[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3057 - loss: 2.0728
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[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3064 - loss: 2.0759
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3067 - loss: 2.0764
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Epoch 35/124

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Epoch 36/124

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[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3096 - loss: 2.0570
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Epoch 37/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3298 - loss: 1.9902 
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[1m104/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3212 - loss: 2.0182
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Epoch 38/124

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[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3173 - loss: 2.0454
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[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3171 - loss: 2.0486
[1m121/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3171 - loss: 2.0485
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3170 - loss: 2.0484
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3170 - loss: 2.0484 - val_accuracy: 0.3794 - val_loss: 2.0271
Epoch 39/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3393 - loss: 1.9985 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3303 - loss: 2.0121
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3263 - loss: 2.0157
[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3245 - loss: 2.0165
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3234 - loss: 2.0173
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3229 - loss: 2.0179 - val_accuracy: 0.3931 - val_loss: 2.0567
Epoch 40/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3188 - loss: 2.0082 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3172 - loss: 2.0175
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3181 - loss: 2.0188
[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3189 - loss: 2.0195
[1m128/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3195 - loss: 2.0192
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3198 - loss: 2.0192 - val_accuracy: 0.3941 - val_loss: 2.0359
Epoch 41/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3160 - loss: 2.0494 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3212 - loss: 2.0388
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[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3249 - loss: 2.0232
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3254 - loss: 2.0191
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3254 - loss: 2.0179 - val_accuracy: 0.3939 - val_loss: 2.0393
Epoch 42/124

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Epoch 43/124

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Saved model to disk.

Accuracy capturado en la ejecución 12: 32.15 [%]
F1-score capturado en la ejecución 12: 31.61 [%]

=== EJECUCIÓN 13 ===

--- TRAIN (ejecución 13) ---

--- TEST (ejecución 13) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 409,231 (1.56 MB)
 Trainable params: 409,231 (1.56 MB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 6, 250)
(18469, 6, 250)

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[1m66/86[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 774us/step
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[1m 59/158[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 883us/step
[1m123/158[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 836us/step
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 40.08 [%]
Global F1 score (validation) = 37.79 [%]
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[[0.00204799 0.00113337 0.00173305 ... 0.03482337 0.00301514 0.00090568]
 [0.00301988 0.00207211 0.00264747 ... 0.07195745 0.00437454 0.00141917]
 [0.00149858 0.00056931 0.00132907 ... 0.04210098 0.00200709 0.00063204]
 ...
 [0.20232542 0.06252872 0.16474436 ... 0.0041315  0.18368214 0.06431841]
 [0.17163162 0.10475264 0.12217101 ... 0.00966976 0.13610472 0.05041364]
 [0.19265406 0.04801616 0.18081357 ... 0.00145536 0.23054747 0.08472041]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 39.65 [%]
Global accuracy score (test) = 33.8 [%]
Global F1 score (train) = 38.34 [%]
Global F1 score (test) = 33.17 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.40      0.28      0.33       184
 CAMINAR CON MÓVIL O LIBRO       0.36      0.33      0.35       184
       CAMINAR USUAL SPEED       0.08      0.02      0.03       184
            CAMINAR ZIGZAG       0.20      0.36      0.26       184
          DE PIE BARRIENDO       0.24      0.20      0.22       184
   DE PIE DOBLANDO TOALLAS       0.40      0.43      0.42       184
    DE PIE MOVIENDO LIBROS       0.36      0.25      0.29       184
          DE PIE USANDO PC       0.22      0.22      0.22       184
        FASE REPOSO CON K5       0.41      0.74      0.53       184
INCREMENTAL CICLOERGOMETRO       0.47      0.64      0.54       184
           SENTADO LEYENDO       0.52      0.30      0.38       184
         SENTADO USANDO PC       0.12      0.10      0.11       184
      SENTADO VIENDO LA TV       0.48      0.38      0.42       184
   SUBIR Y BAJAR ESCALERAS       0.18      0.29      0.22       184
                    TROTAR       0.81      0.55      0.66       161

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

2025-10-28 12:55:55.429578: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-28 12:55:55.440853: 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 00:00:1761652555.454132 1830252 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761652555.458432 1830252 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761652555.468415 1830252 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761652555.468438 1830252 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761652555.468441 1830252 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761652555.468443 1830252 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-28 12:55:55.471725: 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 00:00:1761652557.860839 1830252 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13749 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 409,231 (1.56 MB)
 Trainable params: 409,231 (1.56 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/124
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761652560.419225 1830371 service.cc:152] XLA service 0x732a7c012f60 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761652560.419275 1830371 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-28 12:56:00.473577: 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 00:00:1761652560.774960 1830371 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761652564.476589 1830371 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 2/124

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Epoch 3/124

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Epoch 4/124

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Epoch 5/124

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Epoch 6/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1490 - loss: 2.6842 
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[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1500 - loss: 2.6768
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Epoch 7/124

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[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1677 - loss: 2.6028
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[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.1661 - loss: 2.6052 - val_accuracy: 0.2571 - val_loss: 2.3201
Epoch 8/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1587 - loss: 2.5691 
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[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1668 - loss: 2.5620
[1m121/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1670 - loss: 2.5625
[1m141/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.1673 - loss: 2.5627
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.1674 - loss: 2.5628 - val_accuracy: 0.2760 - val_loss: 2.2963
Epoch 9/124

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[1m 28/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1858 - loss: 2.5476 
[1m 53/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1837 - loss: 2.5401
[1m 79/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1811 - loss: 2.5411
[1m104/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1794 - loss: 2.5410
[1m128/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1788 - loss: 2.5393
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.1786 - loss: 2.5378 - val_accuracy: 0.2912 - val_loss: 2.2743
Epoch 10/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1911 - loss: 2.4955 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1886 - loss: 2.4963
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1882 - loss: 2.4954
[1m 97/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1875 - loss: 2.4961
[1m122/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1868 - loss: 2.4968
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.1869 - loss: 2.4964 - val_accuracy: 0.2976 - val_loss: 2.2684
Epoch 11/124

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[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1753 - loss: 2.4930 
[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1794 - loss: 2.4835
[1m 74/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1832 - loss: 2.4775
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1854 - loss: 2.4745
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1869 - loss: 2.4726
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.1879 - loss: 2.4713 - val_accuracy: 0.3192 - val_loss: 2.2332
Epoch 12/124

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[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1996 - loss: 2.4460 
[1m 48/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2024 - loss: 2.4401
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[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2046 - loss: 2.4372
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2053 - loss: 2.4351
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2055 - loss: 2.4338 - val_accuracy: 0.3284 - val_loss: 2.2221
Epoch 13/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1880 - loss: 2.4408 
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[1m 79/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1936 - loss: 2.4280
[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1958 - loss: 2.4242
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Epoch 14/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2297 - loss: 2.3540 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2266 - loss: 2.3633
[1m 75/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2240 - loss: 2.3675
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2222 - loss: 2.3710
[1m126/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2207 - loss: 2.3739
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2199 - loss: 2.3752 - val_accuracy: 0.3415 - val_loss: 2.2082
Epoch 15/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2405 - loss: 2.3590 
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[1m 74/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2360 - loss: 2.3557
[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2338 - loss: 2.3569
[1m127/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2323 - loss: 2.3574
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2315 - loss: 2.3575 - val_accuracy: 0.3381 - val_loss: 2.2073
Epoch 16/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2241 - loss: 2.3669 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2266 - loss: 2.3582
[1m 75/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2275 - loss: 2.3516
[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2275 - loss: 2.3490
[1m129/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2277 - loss: 2.3469
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2279 - loss: 2.3458 - val_accuracy: 0.3423 - val_loss: 2.1849
Epoch 17/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2319 - loss: 2.3319 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2282 - loss: 2.3338
[1m 75/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2278 - loss: 2.3336
[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2283 - loss: 2.3313
[1m123/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2291 - loss: 2.3296
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2299 - loss: 2.3284 - val_accuracy: 0.3433 - val_loss: 2.1971
Epoch 18/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2109 - loss: 2.3995
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2225 - loss: 2.3365 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2276 - loss: 2.3300
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2292 - loss: 2.3251
[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2302 - loss: 2.3212
[1m128/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2312 - loss: 2.3187
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2320 - loss: 2.3168 - val_accuracy: 0.3476 - val_loss: 2.1885
Epoch 19/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.2812 - loss: 2.2578
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2589 - loss: 2.2720 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2552 - loss: 2.2749
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2542 - loss: 2.2725
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2533 - loss: 2.2729
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2524 - loss: 2.2731
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2518 - loss: 2.2734 - val_accuracy: 0.3446 - val_loss: 2.1627
Epoch 20/124

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[1m 24/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2434 - loss: 2.2883 
[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2424 - loss: 2.2869
[1m 74/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2435 - loss: 2.2852
[1m 97/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2446 - loss: 2.2831
[1m119/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2456 - loss: 2.2802
[1m144/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.2466 - loss: 2.2777
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2467 - loss: 2.2775 - val_accuracy: 0.3530 - val_loss: 2.1624
Epoch 21/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2500 - loss: 2.2251
[1m 23/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2433 - loss: 2.2706 
[1m 47/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2488 - loss: 2.2571
[1m 71/145[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2508 - loss: 2.2500
[1m 97/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2519 - loss: 2.2471
[1m122/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2527 - loss: 2.2459
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.2530 - loss: 2.2454
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2530 - loss: 2.2454 - val_accuracy: 0.3542 - val_loss: 2.1516
Epoch 22/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2734 - loss: 2.2088
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2656 - loss: 2.2160 
[1m 48/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2586 - loss: 2.2298
[1m 72/145[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2556 - loss: 2.2332
[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2556 - loss: 2.2342
[1m121/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2557 - loss: 2.2346
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2563 - loss: 2.2347 - val_accuracy: 0.3639 - val_loss: 2.1508
Epoch 23/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 28ms/step - accuracy: 0.2656 - loss: 2.1913
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2502 - loss: 2.2312 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2533 - loss: 2.2354
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2545 - loss: 2.2393
[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2562 - loss: 2.2378
[1m128/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2577 - loss: 2.2354
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2585 - loss: 2.2338 - val_accuracy: 0.3597 - val_loss: 2.1375
Epoch 24/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2422 - loss: 2.1811
[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2656 - loss: 2.1905 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2688 - loss: 2.1900
[1m 74/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2688 - loss: 2.1923
[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2695 - loss: 2.1936
[1m122/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2696 - loss: 2.1953
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2700 - loss: 2.1965 - val_accuracy: 0.3635 - val_loss: 2.1273
Epoch 25/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.2812 - loss: 2.1002
[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2747 - loss: 2.1874 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2704 - loss: 2.1958
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2699 - loss: 2.1971
[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2703 - loss: 2.1963
[1m127/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2708 - loss: 2.1956
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2711 - loss: 2.1950 - val_accuracy: 0.3585 - val_loss: 2.1300
Epoch 26/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.2656 - loss: 2.1626
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2603 - loss: 2.2015 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2664 - loss: 2.1894
[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2702 - loss: 2.1848
[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2717 - loss: 2.1828
[1m126/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2729 - loss: 2.1802
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2740 - loss: 2.1781 - val_accuracy: 0.3683 - val_loss: 2.1034
Epoch 27/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.3047 - loss: 2.2117
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2760 - loss: 2.1573 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2752 - loss: 2.1658
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[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2773 - loss: 2.1669
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2776 - loss: 2.1667
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Epoch 28/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2712 - loss: 2.1507 
[1m 53/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2767 - loss: 2.1471
[1m 79/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2785 - loss: 2.1461
[1m105/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2793 - loss: 2.1469
[1m132/145[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.2800 - loss: 2.1477
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2803 - loss: 2.1481 - val_accuracy: 0.3637 - val_loss: 2.1195
Epoch 29/124

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[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2899 - loss: 2.1456 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2897 - loss: 2.1460
[1m 75/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2896 - loss: 2.1453
[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2890 - loss: 2.1452
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2889 - loss: 2.1439
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2889 - loss: 2.1432 - val_accuracy: 0.3510 - val_loss: 2.1466
Epoch 30/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.2500 - loss: 2.1037
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2882 - loss: 2.0924 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2883 - loss: 2.1018
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2895 - loss: 2.1055
[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2893 - loss: 2.1091
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2894 - loss: 2.1119
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2895 - loss: 2.1139 - val_accuracy: 0.3637 - val_loss: 2.0972
Epoch 31/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.3047 - loss: 2.0513
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2911 - loss: 2.1138 
[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2906 - loss: 2.1176
[1m 72/145[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2910 - loss: 2.1159
[1m 97/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2915 - loss: 2.1144
[1m122/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2918 - loss: 2.1132
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2922 - loss: 2.1127 - val_accuracy: 0.3605 - val_loss: 2.0926
Epoch 32/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.3516 - loss: 1.9783
[1m 24/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3087 - loss: 2.0846 
[1m 46/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3014 - loss: 2.0957
[1m 71/145[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2991 - loss: 2.1014
[1m 95/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2985 - loss: 2.1044
[1m119/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2980 - loss: 2.1054
[1m142/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.2979 - loss: 2.1058
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2978 - loss: 2.1058 - val_accuracy: 0.3679 - val_loss: 2.0937
Epoch 33/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.3281 - loss: 1.9903
[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3099 - loss: 2.0735 
[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3036 - loss: 2.0812
[1m 73/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3018 - loss: 2.0841
[1m 96/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3012 - loss: 2.0857
[1m121/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3014 - loss: 2.0866
[1m144/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.3017 - loss: 2.0872
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Epoch 34/124

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Epoch 35/124

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Epoch 36/124

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[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3073 - loss: 2.0614
[1m104/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3070 - loss: 2.0621
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Epoch 37/124

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[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3142 - loss: 2.0360
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[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3151 - loss: 2.0387
[1m126/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3149 - loss: 2.0407
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3148 - loss: 2.0421 - val_accuracy: 0.3673 - val_loss: 2.0787
Epoch 38/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3144 - loss: 2.0405 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3148 - loss: 2.0362
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3152 - loss: 2.0366
[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3156 - loss: 2.0377
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3156 - loss: 2.0379
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3158 - loss: 2.0379 - val_accuracy: 0.3784 - val_loss: 2.0612
Epoch 39/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.3281 - loss: 1.9894
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3161 - loss: 2.0318 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3137 - loss: 2.0379
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3135 - loss: 2.0393
[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3141 - loss: 2.0391
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3145 - loss: 2.0395
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3147 - loss: 2.0396 - val_accuracy: 0.3770 - val_loss: 2.0644
Epoch 40/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3188 - loss: 2.0185 
[1m 53/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3181 - loss: 2.0229
[1m 79/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3184 - loss: 2.0251
[1m104/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3196 - loss: 2.0246
[1m130/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3202 - loss: 2.0248
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3202 - loss: 2.0253 - val_accuracy: 0.3730 - val_loss: 2.0684
Epoch 41/124

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[1m 48/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3172 - loss: 2.0032
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Epoch 42/124

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Epoch 43/124

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[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3200 - loss: 2.0172
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Epoch 44/124

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[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3355 - loss: 1.9529 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3318 - loss: 1.9692
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[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3277 - loss: 1.9810
[1m126/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3271 - loss: 1.9833
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Epoch 45/124

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[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3424 - loss: 1.9273 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3386 - loss: 1.9458
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3365 - loss: 1.9569
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3349 - loss: 1.9627
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3342 - loss: 1.9655
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3341 - loss: 1.9665 - val_accuracy: 0.3786 - val_loss: 2.0672
Epoch 46/124

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[1m 22/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3264 - loss: 1.9625 
[1m 47/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3313 - loss: 1.9594
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[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3341 - loss: 1.9637
[1m123/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3348 - loss: 1.9647
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3352 - loss: 1.9658 - val_accuracy: 0.3832 - val_loss: 2.0417
Epoch 47/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3355 - loss: 1.9631 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3350 - loss: 1.9650
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[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3340 - loss: 1.9680
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[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3341 - loss: 1.9675 - val_accuracy: 0.3925 - val_loss: 2.0482
Epoch 48/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3672 - loss: 1.9096 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3604 - loss: 1.9206
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3559 - loss: 1.9278
[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3527 - loss: 1.9323
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Epoch 49/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3310 - loss: 1.9498 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3336 - loss: 1.9530
[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3354 - loss: 1.9499
[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3371 - loss: 1.9461
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Epoch 50/124

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[1m 24/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3526 - loss: 1.8987 
[1m 48/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3504 - loss: 1.9110
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[1m 97/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3478 - loss: 1.9200
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Epoch 51/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3535 - loss: 1.9429 
[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3499 - loss: 1.9422
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[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3485 - loss: 1.9399
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3481 - loss: 1.9394
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3478 - loss: 1.9395 - val_accuracy: 0.3955 - val_loss: 2.0612
Epoch 52/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3592 - loss: 1.9045 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3556 - loss: 1.9125
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3545 - loss: 1.9124
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3540 - loss: 1.9123
[1m123/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3535 - loss: 1.9123
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3530 - loss: 1.9131 - val_accuracy: 0.4048 - val_loss: 2.0270
Epoch 53/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 25ms/step - accuracy: 0.3516 - loss: 1.9913
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3377 - loss: 1.9240 
[1m 48/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3414 - loss: 1.9231
[1m 74/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3448 - loss: 1.9189
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3457 - loss: 1.9194
[1m126/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3465 - loss: 1.9201
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3471 - loss: 1.9201 - val_accuracy: 0.3981 - val_loss: 2.0631
Epoch 54/124

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[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3481 - loss: 1.9110 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3553 - loss: 1.9007
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[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3558 - loss: 1.8968
[1m121/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3556 - loss: 1.8975
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3557 - loss: 1.8980
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3557 - loss: 1.8980 - val_accuracy: 0.3971 - val_loss: 2.0569
Epoch 55/124

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Saved model to disk.

Accuracy capturado en la ejecución 13: 33.8 [%]
F1-score capturado en la ejecución 13: 33.17 [%]

=== EJECUCIÓN 14 ===

--- TRAIN (ejecución 14) ---

--- TEST (ejecución 14) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 409,231 (1.56 MB)
 Trainable params: 409,231 (1.56 MB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 6, 250)
(18469, 6, 250)

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
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[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 17ms/step
[1m59/86[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 870us/step
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step  
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 9ms/step

[1m  1/158[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step
[1m 52/158[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 994us/step
[1m117/158[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 870us/step
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 40.18 [%]
Global F1 score (validation) = 38.33 [%]
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[[0.00241464 0.0012545  0.00178795 ... 0.04000451 0.00306027 0.00071581]
 [0.00209961 0.00138838 0.00157735 ... 0.04444939 0.00273127 0.00050338]
 [0.00114543 0.00034378 0.00097111 ... 0.02279935 0.0017859  0.00038851]
 ...
 [0.18184383 0.04090219 0.22633329 ... 0.00059037 0.2200388  0.07760698]
 [0.2375066  0.06295594 0.19784258 ... 0.00119369 0.20088117 0.051001  ]
 [0.16501772 0.03473791 0.22444671 ... 0.00049334 0.23583168 0.08964588]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 44.29 [%]
Global accuracy score (test) = 32.01 [%]
Global F1 score (train) = 43.58 [%]
Global F1 score (test) = 31.3 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.29      0.35      0.32       184
 CAMINAR CON MÓVIL O LIBRO       0.29      0.36      0.32       184
       CAMINAR USUAL SPEED       0.09      0.05      0.06       184
            CAMINAR ZIGZAG       0.24      0.20      0.22       184
          DE PIE BARRIENDO       0.22      0.21      0.21       184
   DE PIE DOBLANDO TOALLAS       0.27      0.27      0.27       184
    DE PIE MOVIENDO LIBROS       0.33      0.19      0.24       184
          DE PIE USANDO PC       0.24      0.18      0.21       184
        FASE REPOSO CON K5       0.45      0.74      0.56       184
INCREMENTAL CICLOERGOMETRO       0.53      0.60      0.56       184
           SENTADO LEYENDO       0.48      0.36      0.41       184
         SENTADO USANDO PC       0.16      0.10      0.12       184
      SENTADO VIENDO LA TV       0.38      0.38      0.38       184
   SUBIR Y BAJAR ESCALERAS       0.15      0.28      0.19       184
                    TROTAR       0.68      0.55      0.61       161

                  accuracy                           0.32      2737
                 macro avg       0.32      0.32      0.31      2737
              weighted avg       0.32      0.32      0.31      2737

2025-10-28 12:56:47.195621: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-28 12:56:47.207105: 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 00:00:1761652607.220457 1836402 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761652607.224561 1836402 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761652607.234559 1836402 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761652607.234590 1836402 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761652607.234593 1836402 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761652607.234595 1836402 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-28 12:56:47.237870: 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 00:00:1761652609.612630 1836402 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13749 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 409,231 (1.56 MB)
 Trainable params: 409,231 (1.56 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/124
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761652612.135244 1836526 service.cc:152] XLA service 0x7926d4003300 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761652612.135316 1836526 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-28 12:56:52.196018: 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 00:00:1761652612.501598 1836526 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761652616.118206 1836526 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 2/124

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Epoch 3/124

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Epoch 4/124

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Epoch 5/124

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Epoch 6/124

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Epoch 7/124

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Epoch 8/124

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Epoch 9/124

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[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.1768 - loss: 2.5342
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Epoch 10/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1783 - loss: 2.4866 
[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1785 - loss: 2.5004
[1m 72/145[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1780 - loss: 2.5044
[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1778 - loss: 2.5052
[1m122/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1780 - loss: 2.5046
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.1788 - loss: 2.5032 - val_accuracy: 0.2881 - val_loss: 2.2667
Epoch 11/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.1406 - loss: 2.4971
[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1963 - loss: 2.4691 
[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1941 - loss: 2.4743
[1m 74/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1938 - loss: 2.4747
[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1940 - loss: 2.4739
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1946 - loss: 2.4728
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.1948 - loss: 2.4722 - val_accuracy: 0.3159 - val_loss: 2.2376
Epoch 12/124

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[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1926 - loss: 2.4324 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1915 - loss: 2.4429
[1m 74/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1918 - loss: 2.4457
[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1926 - loss: 2.4453
[1m122/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1932 - loss: 2.4446
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.1938 - loss: 2.4438 - val_accuracy: 0.3234 - val_loss: 2.2192
Epoch 13/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2422 - loss: 2.3364
[1m 24/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2149 - loss: 2.3784 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2107 - loss: 2.3903
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[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2098 - loss: 2.3969
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2097 - loss: 2.3984
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Epoch 14/124

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[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2090 - loss: 2.3854
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2096 - loss: 2.3863
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Epoch 15/124

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[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2222 - loss: 2.3800
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[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2234 - loss: 2.3692
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Epoch 16/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2272 - loss: 2.3322 
[1m 47/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2234 - loss: 2.3419
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[1m 97/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2221 - loss: 2.3447
[1m120/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2226 - loss: 2.3446
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2233 - loss: 2.3449 - val_accuracy: 0.3506 - val_loss: 2.1777
Epoch 17/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.1641 - loss: 2.4398
[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2206 - loss: 2.3502 
[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2303 - loss: 2.3348
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[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2327 - loss: 2.3265
[1m123/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2332 - loss: 2.3256
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2336 - loss: 2.3249 - val_accuracy: 0.3496 - val_loss: 2.1782
Epoch 18/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2578 - loss: 2.3396
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2463 - loss: 2.3020 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2412 - loss: 2.3017
[1m 79/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2394 - loss: 2.3023
[1m106/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2388 - loss: 2.3025
[1m132/145[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.2388 - loss: 2.3029
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2388 - loss: 2.3030 - val_accuracy: 0.3478 - val_loss: 2.1719
Epoch 19/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.1953 - loss: 2.3518
[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2440 - loss: 2.2865 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2470 - loss: 2.2855
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2461 - loss: 2.2866
[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2456 - loss: 2.2877
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2452 - loss: 2.2877
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2454 - loss: 2.2872 - val_accuracy: 0.3452 - val_loss: 2.1812
Epoch 20/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2422 - loss: 2.2788
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2408 - loss: 2.2807 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2426 - loss: 2.2736
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2438 - loss: 2.2698
[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2442 - loss: 2.2685
[1m128/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2448 - loss: 2.2675
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2450 - loss: 2.2672 - val_accuracy: 0.3460 - val_loss: 2.1600
Epoch 21/124

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[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2413 - loss: 2.2607 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2441 - loss: 2.2644
[1m 74/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2456 - loss: 2.2658
[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2469 - loss: 2.2646
[1m122/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2485 - loss: 2.2622
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.2496 - loss: 2.2601
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2496 - loss: 2.2600 - val_accuracy: 0.3556 - val_loss: 2.1561
Epoch 22/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 25ms/step - accuracy: 0.2188 - loss: 2.2899
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2525 - loss: 2.2407 
[1m 53/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2560 - loss: 2.2326
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2561 - loss: 2.2346
[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2563 - loss: 2.2357
[1m127/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2564 - loss: 2.2362
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2565 - loss: 2.2360 - val_accuracy: 0.3587 - val_loss: 2.1528
Epoch 23/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.2812 - loss: 2.1762
[1m 22/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2741 - loss: 2.1911 
[1m 48/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2691 - loss: 2.2009
[1m 68/145[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2688 - loss: 2.2035
[1m 93/145[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2690 - loss: 2.2043
[1m119/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2692 - loss: 2.2050
[1m142/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.2688 - loss: 2.2058
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2687 - loss: 2.2059 - val_accuracy: 0.3556 - val_loss: 2.1517
Epoch 24/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.2812 - loss: 2.1566
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2732 - loss: 2.1762 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2718 - loss: 2.1834
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2697 - loss: 2.1905
[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2690 - loss: 2.1934
[1m122/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2690 - loss: 2.1944
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2691 - loss: 2.1952 - val_accuracy: 0.3659 - val_loss: 2.1321
Epoch 25/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2188 - loss: 2.2362
[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2534 - loss: 2.2148 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2576 - loss: 2.2126
[1m 75/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2630 - loss: 2.2069
[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2658 - loss: 2.2033
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2670 - loss: 2.2019
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2682 - loss: 2.2003 - val_accuracy: 0.3671 - val_loss: 2.1310
Epoch 26/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.2578 - loss: 2.2141
[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2607 - loss: 2.2176 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2681 - loss: 2.2013
[1m 75/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2711 - loss: 2.1924
[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2731 - loss: 2.1872
[1m122/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2739 - loss: 2.1849
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2744 - loss: 2.1837 - val_accuracy: 0.3701 - val_loss: 2.1228
Epoch 27/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2969 - loss: 2.1106
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2671 - loss: 2.1944 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2704 - loss: 2.1814
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2720 - loss: 2.1752
[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2733 - loss: 2.1729
[1m126/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2744 - loss: 2.1717
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2753 - loss: 2.1708 - val_accuracy: 0.3568 - val_loss: 2.1426
Epoch 28/124

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[1m 94/145[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2835 - loss: 2.1463
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Epoch 29/124

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[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2935 - loss: 2.1346
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Epoch 30/124

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[1m 48/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3031 - loss: 2.1333
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[1m 94/145[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2979 - loss: 2.1334
[1m118/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2961 - loss: 2.1333
[1m143/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.2956 - loss: 2.1317
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Epoch 31/124

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[1m 23/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2811 - loss: 2.1770 
[1m 46/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2849 - loss: 2.1518
[1m 71/145[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2875 - loss: 2.1406
[1m 95/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2884 - loss: 2.1355
[1m119/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2889 - loss: 2.1323
[1m143/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.2892 - loss: 2.1303
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2893 - loss: 2.1301 - val_accuracy: 0.3669 - val_loss: 2.0837
Epoch 32/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 29ms/step - accuracy: 0.2500 - loss: 2.0173
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2765 - loss: 2.1109 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2883 - loss: 2.0994
[1m 74/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2919 - loss: 2.0974
[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2933 - loss: 2.0975
[1m122/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2938 - loss: 2.0982
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2943 - loss: 2.0987 - val_accuracy: 0.3685 - val_loss: 2.0994
Epoch 33/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2979 - loss: 2.1114 
[1m 48/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2978 - loss: 2.1063
[1m 72/145[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2980 - loss: 2.1021
[1m 95/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2977 - loss: 2.0998
[1m117/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2976 - loss: 2.0985
[1m140/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.2974 - loss: 2.0973
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2975 - loss: 2.0969 - val_accuracy: 0.3744 - val_loss: 2.0733
Epoch 34/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2902 - loss: 2.0788 
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Epoch 35/124

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Epoch 36/124

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Epoch 37/124

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[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3177 - loss: 2.0398
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3164 - loss: 2.0408
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Epoch 38/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3266 - loss: 2.0524 
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[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3170 - loss: 2.0506
[1m104/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3144 - loss: 2.0509
[1m128/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3133 - loss: 2.0496
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3128 - loss: 2.0487 - val_accuracy: 0.3899 - val_loss: 2.0494
Epoch 39/124

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[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3137 - loss: 2.0392 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3158 - loss: 2.0311
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[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3182 - loss: 2.0266
[1m123/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3185 - loss: 2.0268
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3186 - loss: 2.0269 - val_accuracy: 0.3901 - val_loss: 2.0201
Epoch 40/124

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[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3304 - loss: 1.9881
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[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3266 - loss: 2.0008
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Epoch 41/124

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[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3212 - loss: 2.0015
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Epoch 42/124

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Epoch 43/124

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Epoch 44/124

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[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3319 - loss: 1.9771
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[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3294 - loss: 1.9812 - val_accuracy: 0.3848 - val_loss: 2.0289

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[1m61/86[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 840us/step  
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step 
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Saved model to disk.

Accuracy capturado en la ejecución 14: 32.01 [%]
F1-score capturado en la ejecución 14: 31.3 [%]

=== EJECUCIÓN 15 ===

--- TRAIN (ejecución 15) ---

--- TEST (ejecución 15) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 409,231 (1.56 MB)
 Trainable params: 409,231 (1.56 MB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 6, 250)
(18469, 6, 250)

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[1m 56/158[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 913us/step
[1m127/158[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 800us/step
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 38.48 [%]
Global F1 score (validation) = 35.76 [%]
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[[0.00161168 0.00062179 0.00126077 ... 0.03590788 0.0019893  0.00069127]
 [0.00239949 0.00123192 0.00203766 ... 0.06297693 0.00298222 0.00092066]
 [0.00259855 0.00067696 0.00208582 ... 0.0438461  0.00334361 0.00102176]
 ...
 [0.19752742 0.04564681 0.1753765  ... 0.00135188 0.22832114 0.08964045]
 [0.19467229 0.1063947  0.12624383 ... 0.01291744 0.14479575 0.05095805]
 [0.19934015 0.04819069 0.17499305 ... 0.00118509 0.24227874 0.08111696]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 38.83 [%]
Global accuracy score (test) = 32.08 [%]
Global F1 score (train) = 37.2 [%]
Global F1 score (test) = 30.8 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.25      0.12      0.16       184
 CAMINAR CON MÓVIL O LIBRO       0.42      0.33      0.37       184
       CAMINAR USUAL SPEED       0.15      0.04      0.07       184
            CAMINAR ZIGZAG       0.08      0.06      0.07       184
          DE PIE BARRIENDO       0.25      0.23      0.24       184
   DE PIE DOBLANDO TOALLAS       0.35      0.44      0.39       184
    DE PIE MOVIENDO LIBROS       0.28      0.20      0.23       184
          DE PIE USANDO PC       0.29      0.34      0.31       184
        FASE REPOSO CON K5       0.41      0.74      0.53       184
INCREMENTAL CICLOERGOMETRO       0.53      0.61      0.57       184
           SENTADO LEYENDO       0.44      0.30      0.35       184
         SENTADO USANDO PC       0.12      0.10      0.11       184
      SENTADO VIENDO LA TV       0.38      0.30      0.33       184
   SUBIR Y BAJAR ESCALERAS       0.19      0.48      0.27       184
                    TROTAR       0.71      0.55      0.62       161

                  accuracy                           0.32      2737
                 macro avg       0.32      0.32      0.31      2737
              weighted avg       0.32      0.32      0.31      2737

2025-10-28 12:57:34.252068: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-28 12:57:34.263209: 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 00:00:1761652654.276578 1841565 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761652654.280616 1841565 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761652654.290543 1841565 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761652654.290564 1841565 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761652654.290567 1841565 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761652654.290569 1841565 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-28 12:57:34.293611: 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 00:00:1761652656.682882 1841565 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13749 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 409,231 (1.56 MB)
 Trainable params: 409,231 (1.56 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/124
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761652659.238136 1841679 service.cc:152] XLA service 0x761b24005740 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761652659.238187 1841679 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-28 12:57:39.296632: 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 00:00:1761652659.604687 1841679 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761652663.233233 1841679 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13:51[0m 6s/step - accuracy: 0.0938 - loss: 3.1684
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[1m140/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.0799 - loss: 3.1593
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 26ms/step - accuracy: 0.0802 - loss: 3.15712025-10-28 12:57:48.847224: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_316', 12 bytes spill stores, 12 bytes spill loads


[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m12s[0m 42ms/step - accuracy: 0.0802 - loss: 3.1566 - val_accuracy: 0.1801 - val_loss: 2.4874
Epoch 2/124

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[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.1066 - loss: 2.9539 - val_accuracy: 0.2003 - val_loss: 2.4361
Epoch 3/124

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Epoch 4/124

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Epoch 5/124

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Epoch 6/124

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Epoch 7/124

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[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1674 - loss: 2.6142
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Epoch 8/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1566 - loss: 2.5775 
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[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1641 - loss: 2.5760
[1m127/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1652 - loss: 2.5766
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.1657 - loss: 2.5767 - val_accuracy: 0.2603 - val_loss: 2.3165
Epoch 9/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1754 - loss: 2.5552 
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[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1826 - loss: 2.5460
[1m127/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1826 - loss: 2.5446
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.1827 - loss: 2.5439 - val_accuracy: 0.2698 - val_loss: 2.2781
Epoch 10/124

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[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1879 - loss: 2.5156
[1m123/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1885 - loss: 2.5128
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Epoch 11/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2266 - loss: 2.4758
[1m 23/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2136 - loss: 2.4729 
[1m 48/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2064 - loss: 2.4749
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[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2020 - loss: 2.4712
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Epoch 12/124

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[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1858 - loss: 2.4811
[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1883 - loss: 2.4758
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[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.1913 - loss: 2.4686 - val_accuracy: 0.3173 - val_loss: 2.2149
Epoch 13/124

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[1m 24/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2230 - loss: 2.3982 
[1m 47/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2217 - loss: 2.4026
[1m 72/145[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2200 - loss: 2.4043
[1m 95/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2188 - loss: 2.4053
[1m119/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2172 - loss: 2.4077
[1m143/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.2161 - loss: 2.4089
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2159 - loss: 2.4090 - val_accuracy: 0.3302 - val_loss: 2.2129
Epoch 14/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.1875 - loss: 2.4199
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2071 - loss: 2.4043 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2104 - loss: 2.3941
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2129 - loss: 2.3919
[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2144 - loss: 2.3895
[1m127/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2152 - loss: 2.3878
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2155 - loss: 2.3868 - val_accuracy: 0.3377 - val_loss: 2.1787
Epoch 15/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.2266 - loss: 2.3477
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2214 - loss: 2.3651 
[1m 53/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2217 - loss: 2.3627
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2212 - loss: 2.3617
[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2203 - loss: 2.3635
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2202 - loss: 2.3638
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2203 - loss: 2.3634 - val_accuracy: 0.3401 - val_loss: 2.1682
Epoch 16/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.2734 - loss: 2.1984
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2242 - loss: 2.3307 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2247 - loss: 2.3308
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2252 - loss: 2.3307
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2252 - loss: 2.3317
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2253 - loss: 2.3326
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2257 - loss: 2.3331 - val_accuracy: 0.3464 - val_loss: 2.1617
Epoch 17/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.1953 - loss: 2.2856
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2275 - loss: 2.3046 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2299 - loss: 2.3055
[1m 75/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2304 - loss: 2.3071
[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2301 - loss: 2.3090
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2304 - loss: 2.3101
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2305 - loss: 2.3113 - val_accuracy: 0.3508 - val_loss: 2.1577
Epoch 18/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2492 - loss: 2.3071 
[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2470 - loss: 2.3089
[1m 75/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2456 - loss: 2.3063
[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2455 - loss: 2.3041
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Epoch 19/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2401 - loss: 2.3124 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2429 - loss: 2.2973
[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2436 - loss: 2.2935
[1m105/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2446 - loss: 2.2905
[1m131/145[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.2451 - loss: 2.2884
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2454 - loss: 2.2874 - val_accuracy: 0.3578 - val_loss: 2.1214
Epoch 20/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2587 - loss: 2.2591 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2552 - loss: 2.2599
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[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2531 - loss: 2.2603
[1m121/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2528 - loss: 2.2598
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.2526 - loss: 2.2593
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Epoch 21/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2662 - loss: 2.2241 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2597 - loss: 2.2372
[1m 72/145[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2582 - loss: 2.2396
[1m 97/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2577 - loss: 2.2404
[1m120/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2575 - loss: 2.2411
[1m144/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.2576 - loss: 2.2410
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2576 - loss: 2.2409 - val_accuracy: 0.3603 - val_loss: 2.1066
Epoch 22/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.2188 - loss: 2.2797
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2542 - loss: 2.2411 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2567 - loss: 2.2347
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2580 - loss: 2.2335
[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2590 - loss: 2.2329
[1m128/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2597 - loss: 2.2323
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2600 - loss: 2.2320 - val_accuracy: 0.3564 - val_loss: 2.1092
Epoch 23/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.2500 - loss: 2.3386
[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2596 - loss: 2.2439 
[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2631 - loss: 2.2355
[1m 74/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2645 - loss: 2.2299
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2649 - loss: 2.2265
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2650 - loss: 2.2238
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2650 - loss: 2.2223 - val_accuracy: 0.3589 - val_loss: 2.0850
Epoch 24/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2747 - loss: 2.1839 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2693 - loss: 2.1932
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[1m 95/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2650 - loss: 2.2001
[1m120/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2655 - loss: 2.1997
[1m140/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.2662 - loss: 2.1986
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2663 - loss: 2.1984 - val_accuracy: 0.3621 - val_loss: 2.0921
Epoch 25/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2734 - loss: 2.1867
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2634 - loss: 2.1953 
[1m 48/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2690 - loss: 2.1876
[1m 73/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2729 - loss: 2.1810
[1m 97/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2750 - loss: 2.1775
[1m119/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2755 - loss: 2.1763
[1m143/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.2755 - loss: 2.1765
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2755 - loss: 2.1766 - val_accuracy: 0.3657 - val_loss: 2.0715
Epoch 26/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.2891 - loss: 2.1555
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2730 - loss: 2.1725 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2781 - loss: 2.1654
[1m 75/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2788 - loss: 2.1658
[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2786 - loss: 2.1659
[1m122/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2784 - loss: 2.1658
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2785 - loss: 2.1651 - val_accuracy: 0.3695 - val_loss: 2.0872
Epoch 27/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2656 - loss: 2.2018
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2954 - loss: 2.1580 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2937 - loss: 2.1584
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2916 - loss: 2.1591
[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2896 - loss: 2.1607
[1m127/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2882 - loss: 2.1613
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2876 - loss: 2.1614 - val_accuracy: 0.3629 - val_loss: 2.0793
Epoch 28/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2500 - loss: 2.3908
[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2903 - loss: 2.1648 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2904 - loss: 2.1551
[1m 74/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2905 - loss: 2.1529
[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2903 - loss: 2.1523
[1m126/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2902 - loss: 2.1517
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2902 - loss: 2.1512 - val_accuracy: 0.3619 - val_loss: 2.0733
Epoch 29/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2734 - loss: 2.1192
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2945 - loss: 2.1108 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2889 - loss: 2.1271
[1m 74/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2871 - loss: 2.1315
[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2871 - loss: 2.1316
[1m122/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2871 - loss: 2.1313
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2871 - loss: 2.1310 - val_accuracy: 0.3687 - val_loss: 2.0545
Epoch 30/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.3125 - loss: 2.1121
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2867 - loss: 2.1383 
[1m 54/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2850 - loss: 2.1345
[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2854 - loss: 2.1318
[1m105/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2861 - loss: 2.1295
[1m130/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2870 - loss: 2.1282
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2875 - loss: 2.1276 - val_accuracy: 0.3713 - val_loss: 2.0483
Epoch 31/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 26ms/step - accuracy: 0.3125 - loss: 2.0868
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2899 - loss: 2.1093 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2914 - loss: 2.1047
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Epoch 32/124

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Epoch 33/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2966 - loss: 2.0894 
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[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2993 - loss: 2.0901
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Epoch 34/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2898 - loss: 2.1013 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2945 - loss: 2.0922
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2983 - loss: 2.0849
[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3005 - loss: 2.0805
[1m128/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3013 - loss: 2.0786
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3017 - loss: 2.0778 - val_accuracy: 0.3685 - val_loss: 2.0279
Epoch 35/124

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[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2960 - loss: 2.0926
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[1m 97/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3009 - loss: 2.0812
[1m122/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3026 - loss: 2.0766
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3038 - loss: 2.0731
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3039 - loss: 2.0730 - val_accuracy: 0.3754 - val_loss: 2.0219
Epoch 36/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3099 - loss: 2.0515 
[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3096 - loss: 2.0492
[1m 73/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3098 - loss: 2.0488
[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3101 - loss: 2.0489
[1m122/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3103 - loss: 2.0487
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3104 - loss: 2.0492 - val_accuracy: 0.3776 - val_loss: 2.0121
Epoch 37/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3077 - loss: 2.0616 
[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3075 - loss: 2.0554
[1m 73/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3066 - loss: 2.0535
[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3074 - loss: 2.0505
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3082 - loss: 2.0485
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3086 - loss: 2.0472 - val_accuracy: 0.3762 - val_loss: 2.0204
Epoch 38/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3103 - loss: 2.0462 
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Epoch 39/124

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Epoch 40/124

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Epoch 41/124

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[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3363 - loss: 1.9769
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Epoch 42/124

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[1m106/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3263 - loss: 1.9971
[1m131/145[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.3266 - loss: 1.9974
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3267 - loss: 1.9975 - val_accuracy: 0.3913 - val_loss: 1.9926
Epoch 43/124

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[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3263 - loss: 2.0030
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[1m 94/145[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3316 - loss: 1.9867
[1m120/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3323 - loss: 1.9842
[1m144/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.3326 - loss: 1.9832
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3326 - loss: 1.9832 - val_accuracy: 0.3851 - val_loss: 2.0076
Epoch 44/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3323 - loss: 1.9643 
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[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3312 - loss: 1.9752
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Epoch 45/124

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[1m 48/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3454 - loss: 1.9659
[1m 71/145[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3427 - loss: 1.9705
[1m 96/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3412 - loss: 1.9717
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Epoch 46/124

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[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3387 - loss: 1.9519
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Epoch 47/124

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[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3437 - loss: 1.9445
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[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3432 - loss: 1.9481
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Epoch 48/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3401 - loss: 1.9241 
[1m 54/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3356 - loss: 1.9347
[1m 80/145[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3361 - loss: 1.9364
[1m106/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3363 - loss: 1.9376
[1m131/145[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.3366 - loss: 1.9388
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3368 - loss: 1.9394 - val_accuracy: 0.3943 - val_loss: 1.9854
Epoch 49/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3329 - loss: 1.9355 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3431 - loss: 1.9241
[1m 75/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3452 - loss: 1.9240
[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3459 - loss: 1.9253
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3464 - loss: 1.9262
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3468 - loss: 1.9268 - val_accuracy: 0.4024 - val_loss: 1.9906
Epoch 50/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.3516 - loss: 1.9234
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3521 - loss: 1.9308 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3504 - loss: 1.9270
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3496 - loss: 1.9268
[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3487 - loss: 1.9277
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3483 - loss: 1.9273
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3481 - loss: 1.9267 - val_accuracy: 0.3967 - val_loss: 1.9770
Epoch 51/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.3750 - loss: 1.7787
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3518 - loss: 1.9154 
[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3527 - loss: 1.9101
[1m 73/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3529 - loss: 1.9100
[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3525 - loss: 1.9116
[1m123/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3521 - loss: 1.9136
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3518 - loss: 1.9153 - val_accuracy: 0.4018 - val_loss: 2.0045
Epoch 52/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3512 - loss: 1.8675 
[1m 53/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3526 - loss: 1.8792
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[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3511 - loss: 1.8905
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Epoch 53/124

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Epoch 54/124

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[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3570 - loss: 1.9070
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Epoch 55/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3539 - loss: 1.9114 
[1m 53/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3567 - loss: 1.9059
[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3580 - loss: 1.9020
[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3585 - loss: 1.8990
[1m123/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3584 - loss: 1.8975
[1m144/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.3585 - loss: 1.8964
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3585 - loss: 1.8963 - val_accuracy: 0.4070 - val_loss: 1.9975

[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:15[0m 891ms/step
[1m65/86[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 787us/step  
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step 
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 11ms/step
Saved model to disk.

Accuracy capturado en la ejecución 15: 32.08 [%]
F1-score capturado en la ejecución 15: 30.8 [%]

=== EJECUCIÓN 16 ===

--- TRAIN (ejecución 16) ---

--- TEST (ejecución 16) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 409,231 (1.56 MB)
 Trainable params: 409,231 (1.56 MB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 6, 250)
(18469, 6, 250)

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[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step 
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[1m  1/158[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 17ms/step
[1m 54/158[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 945us/step
[1m119/158[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 851us/step
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 40.7 [%]
Global F1 score (validation) = 38.77 [%]
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[[1.5517052e-03 6.9555262e-04 1.1911488e-03 ... 3.6835514e-02
  1.9937698e-03 3.2730112e-04]
 [1.8745726e-03 1.4311726e-03 1.8897821e-03 ... 8.5154258e-02
  2.8099259e-03 4.0647923e-04]
 [5.2954303e-04 1.6826506e-04 3.8849321e-04 ... 1.6194245e-02
  6.4749370e-04 1.6223041e-04]
 ...
 [1.5286592e-01 3.5953995e-02 2.1721321e-01 ... 6.0083350e-04
  2.2780916e-01 1.0313668e-01]
 [2.0921628e-01 8.1311673e-02 1.7707402e-01 ... 1.4142254e-03
  1.7097600e-01 7.8510225e-02]
 [2.0497769e-01 4.5410085e-02 1.8099806e-01 ... 7.5952400e-04
  2.5666571e-01 6.4340085e-02]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 43.97 [%]
Global accuracy score (test) = 35.33 [%]
Global F1 score (train) = 43.38 [%]
Global F1 score (test) = 34.66 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.26      0.33      0.29       184
 CAMINAR CON MÓVIL O LIBRO       0.41      0.34      0.37       184
       CAMINAR USUAL SPEED       0.13      0.05      0.07       184
            CAMINAR ZIGZAG       0.20      0.28      0.24       184
          DE PIE BARRIENDO       0.36      0.33      0.34       184
   DE PIE DOBLANDO TOALLAS       0.38      0.38      0.38       184
    DE PIE MOVIENDO LIBROS       0.34      0.29      0.32       184
          DE PIE USANDO PC       0.26      0.24      0.25       184
        FASE REPOSO CON K5       0.44      0.74      0.56       184
INCREMENTAL CICLOERGOMETRO       0.53      0.63      0.58       184
           SENTADO LEYENDO       0.45      0.30      0.36       184
         SENTADO USANDO PC       0.24      0.15      0.18       184
      SENTADO VIENDO LA TV       0.50      0.47      0.49       184
   SUBIR Y BAJAR ESCALERAS       0.16      0.22      0.18       184
                    TROTAR       0.63      0.56      0.59       161

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

2025-10-28 12:58:25.928222: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-28 12:58:25.939635: 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 00:00:1761652705.952903 1847858 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761652705.957196 1847858 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761652705.967256 1847858 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761652705.967283 1847858 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761652705.967286 1847858 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761652705.967289 1847858 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-28 12:58:25.970739: 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 00:00:1761652708.342847 1847858 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13749 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 409,231 (1.56 MB)
 Trainable params: 409,231 (1.56 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/124
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761652711.037077 1847989 service.cc:152] XLA service 0x76c9a8012310 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761652711.037154 1847989 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-28 12:58:31.092484: 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 00:00:1761652711.389342 1847989 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761652715.030721 1847989 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m14:12[0m 6s/step - accuracy: 0.0547 - loss: 3.1703
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[1m 91/145[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.0698 - loss: 3.1868
[1m117/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.0721 - loss: 3.1720
[1m140/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.0739 - loss: 3.1606
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 27ms/step - accuracy: 0.0743 - loss: 3.15822025-10-28 12:58:40.738796: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_316', 12 bytes spill stores, 12 bytes spill loads


[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m12s[0m 43ms/step - accuracy: 0.0743 - loss: 3.1577 - val_accuracy: 0.1894 - val_loss: 2.4278
Epoch 2/124

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[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1039 - loss: 2.9964
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[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.1029 - loss: 2.9759
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.1029 - loss: 2.9757 - val_accuracy: 0.2059 - val_loss: 2.3647
Epoch 3/124

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Epoch 4/124

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Epoch 5/124

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Epoch 6/124

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Epoch 7/124

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[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1619 - loss: 2.6311
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1620 - loss: 2.6290
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Epoch 8/124

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[1m 48/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1751 - loss: 2.5638
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[1m 93/145[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1746 - loss: 2.5631
[1m116/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1748 - loss: 2.5625
[1m140/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.1747 - loss: 2.5619
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.1747 - loss: 2.5617 - val_accuracy: 0.2837 - val_loss: 2.2534
Epoch 9/124

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[1m 93/145[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1788 - loss: 2.5423
[1m116/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1781 - loss: 2.5423
[1m143/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.1779 - loss: 2.5413
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Epoch 10/124

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[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.1939 - loss: 2.4865
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.1939 - loss: 2.4866 - val_accuracy: 0.3111 - val_loss: 2.2202
Epoch 11/124

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[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1837 - loss: 2.4973 
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[1m 97/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1934 - loss: 2.4731
[1m121/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1939 - loss: 2.4709
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.1945 - loss: 2.4694
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Epoch 12/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2266 - loss: 2.3805
[1m 24/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2060 - loss: 2.4542 
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[1m 96/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2040 - loss: 2.4455
[1m123/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2031 - loss: 2.4432
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2029 - loss: 2.4412 - val_accuracy: 0.3325 - val_loss: 2.1979
Epoch 13/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2422 - loss: 2.3525
[1m 24/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2124 - loss: 2.4010 
[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2077 - loss: 2.4062
[1m 75/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2067 - loss: 2.4063
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2066 - loss: 2.4060
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2068 - loss: 2.4058
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2070 - loss: 2.4053 - val_accuracy: 0.3425 - val_loss: 2.1892
Epoch 14/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2031 - loss: 2.3871
[1m 28/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2163 - loss: 2.3623 
[1m 54/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2186 - loss: 2.3650
[1m 79/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2192 - loss: 2.3686
[1m105/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2197 - loss: 2.3703
[1m131/145[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.2201 - loss: 2.3710
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2203 - loss: 2.3710 - val_accuracy: 0.3413 - val_loss: 2.1808
Epoch 15/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2422 - loss: 2.4535
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2221 - loss: 2.3788 
[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2215 - loss: 2.3723
[1m 75/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2211 - loss: 2.3687
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2208 - loss: 2.3665
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2212 - loss: 2.3640
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2213 - loss: 2.3626 - val_accuracy: 0.3540 - val_loss: 2.1575
Epoch 16/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.2188 - loss: 2.2303
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2114 - loss: 2.3740 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2177 - loss: 2.3642
[1m 75/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2213 - loss: 2.3582
[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2233 - loss: 2.3546
[1m122/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2246 - loss: 2.3516
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2256 - loss: 2.3488 - val_accuracy: 0.3510 - val_loss: 2.1595
Epoch 17/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2266 - loss: 2.2312
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2278 - loss: 2.3235 
[1m 48/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2341 - loss: 2.3227
[1m 72/145[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2362 - loss: 2.3207
[1m 95/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2370 - loss: 2.3194
[1m118/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2372 - loss: 2.3181
[1m144/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.2371 - loss: 2.3173
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2371 - loss: 2.3172 - val_accuracy: 0.3500 - val_loss: 2.1487
Epoch 18/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2656 - loss: 2.1869
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2438 - loss: 2.2879 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2433 - loss: 2.2938
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2430 - loss: 2.2966
[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2431 - loss: 2.2975
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[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2439 - loss: 2.2981 - val_accuracy: 0.3532 - val_loss: 2.1554
Epoch 19/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 28ms/step - accuracy: 0.2422 - loss: 2.2815
[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2472 - loss: 2.2919 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2483 - loss: 2.2877
[1m 72/145[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2494 - loss: 2.2823
[1m 96/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2500 - loss: 2.2788
[1m121/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2506 - loss: 2.2761
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.2504 - loss: 2.2752
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2503 - loss: 2.2752 - val_accuracy: 0.3637 - val_loss: 2.1203
Epoch 20/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.2500 - loss: 2.2110
[1m 24/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2504 - loss: 2.2701 
[1m 48/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2492 - loss: 2.2783
[1m 72/145[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2502 - loss: 2.2773
[1m 97/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2521 - loss: 2.2743
[1m122/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2533 - loss: 2.2711
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.2540 - loss: 2.2685
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2540 - loss: 2.2684 - val_accuracy: 0.3536 - val_loss: 2.1218
Epoch 21/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.1797 - loss: 2.3274
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2488 - loss: 2.2543 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2542 - loss: 2.2436
[1m 75/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2559 - loss: 2.2397
[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2565 - loss: 2.2389
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2570 - loss: 2.2388
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2571 - loss: 2.2395 - val_accuracy: 0.3514 - val_loss: 2.1341
Epoch 22/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.3125 - loss: 2.1202
[1m 24/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2785 - loss: 2.2102 
[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2722 - loss: 2.2224
[1m 72/145[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2700 - loss: 2.2254
[1m 96/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2694 - loss: 2.2245
[1m120/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2688 - loss: 2.2243
[1m142/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.2682 - loss: 2.2246
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2681 - loss: 2.2248 - val_accuracy: 0.3587 - val_loss: 2.1048
Epoch 23/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2422 - loss: 2.2028
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2574 - loss: 2.1911 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2604 - loss: 2.1978
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2621 - loss: 2.2011
[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2617 - loss: 2.2053
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2617 - loss: 2.2079
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2619 - loss: 2.2092 - val_accuracy: 0.3476 - val_loss: 2.1291
Epoch 24/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2656 - loss: 2.1566
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2821 - loss: 2.2043 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2769 - loss: 2.2037
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2750 - loss: 2.2027
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2737 - loss: 2.2039
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2729 - loss: 2.2038
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2724 - loss: 2.2035 - val_accuracy: 0.3514 - val_loss: 2.1036
Epoch 25/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.2969 - loss: 2.1454
[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2688 - loss: 2.1903 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2725 - loss: 2.1844
[1m 75/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2736 - loss: 2.1826
[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2740 - loss: 2.1824
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2741 - loss: 2.1827
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2744 - loss: 2.1828 - val_accuracy: 0.3585 - val_loss: 2.0844
Epoch 26/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2500 - loss: 2.1477
[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2657 - loss: 2.1784 
[1m 48/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2694 - loss: 2.1767
[1m 71/145[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2708 - loss: 2.1770
[1m 94/145[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2719 - loss: 2.1761
[1m118/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2729 - loss: 2.1743
[1m142/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.2740 - loss: 2.1729
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2741 - loss: 2.1727 - val_accuracy: 0.3581 - val_loss: 2.0884
Epoch 27/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.3281 - loss: 2.0102
[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2747 - loss: 2.1483 
[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2757 - loss: 2.1553
[1m 73/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2765 - loss: 2.1599
[1m 97/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2763 - loss: 2.1610
[1m121/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2764 - loss: 2.1608
[1m144/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.2765 - loss: 2.1604
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2765 - loss: 2.1604 - val_accuracy: 0.3508 - val_loss: 2.0937
Epoch 28/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.2891 - loss: 2.1002
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2867 - loss: 2.1434 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2844 - loss: 2.1426
[1m 75/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2841 - loss: 2.1422
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2850 - loss: 2.1409
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2854 - loss: 2.1406
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2853 - loss: 2.1410 - val_accuracy: 0.3585 - val_loss: 2.0902
Epoch 29/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.2812 - loss: 2.2787
[1m 24/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2952 - loss: 2.1351 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2941 - loss: 2.1352
[1m 75/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2915 - loss: 2.1352
[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2905 - loss: 2.1351
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2902 - loss: 2.1353
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2901 - loss: 2.1351 - val_accuracy: 0.3591 - val_loss: 2.0722
Epoch 30/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2266 - loss: 2.2549
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2888 - loss: 2.1303 
[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2913 - loss: 2.1297
[1m 73/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2916 - loss: 2.1286
[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2912 - loss: 2.1285
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2909 - loss: 2.1274
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2907 - loss: 2.1266 - val_accuracy: 0.3635 - val_loss: 2.0650
Epoch 31/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.3984 - loss: 1.8644
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Epoch 32/124

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[1m 97/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2979 - loss: 2.1043
[1m121/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2977 - loss: 2.1046
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Epoch 33/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2109 - loss: 2.2454
[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2921 - loss: 2.1145 
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[1m 75/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2979 - loss: 2.0939
[1m 97/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2982 - loss: 2.0924
[1m121/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2981 - loss: 2.0915
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Epoch 34/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3142 - loss: 2.0891 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3122 - loss: 2.0880
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3113 - loss: 2.0859
[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3109 - loss: 2.0830
[1m129/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3103 - loss: 2.0807
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3096 - loss: 2.0802 - val_accuracy: 0.3627 - val_loss: 2.0626
Epoch 35/124

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[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2980 - loss: 2.0460 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3012 - loss: 2.0558
[1m 71/145[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3042 - loss: 2.0568
[1m 96/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3058 - loss: 2.0584
[1m119/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3061 - loss: 2.0608
[1m141/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.3065 - loss: 2.0619
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3066 - loss: 2.0622 - val_accuracy: 0.3655 - val_loss: 2.0492
Epoch 36/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.3047 - loss: 2.1817
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2910 - loss: 2.1145 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2906 - loss: 2.1064
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2904 - loss: 2.1019
[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2922 - loss: 2.0953
[1m126/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2944 - loss: 2.0887
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2957 - loss: 2.0847 - val_accuracy: 0.3663 - val_loss: 2.0403
Epoch 37/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3032 - loss: 2.0452 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3051 - loss: 2.0496
[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3056 - loss: 2.0512
[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3057 - loss: 2.0531
[1m128/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3060 - loss: 2.0534
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Epoch 38/124

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[1m 79/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3127 - loss: 2.0426
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Epoch 39/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3251 - loss: 2.0132 
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[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3234 - loss: 2.0253
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Epoch 40/124

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[1m 24/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3137 - loss: 2.0243 
[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3148 - loss: 2.0267
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[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3164 - loss: 2.0256
[1m126/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3165 - loss: 2.0250
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3166 - loss: 2.0245 - val_accuracy: 0.3667 - val_loss: 2.0259
Epoch 41/124

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[1m 28/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3281 - loss: 1.9946 
[1m 53/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3267 - loss: 1.9989
[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3254 - loss: 2.0026
[1m104/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3250 - loss: 2.0030
[1m129/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3249 - loss: 2.0040
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3248 - loss: 2.0050 - val_accuracy: 0.3687 - val_loss: 2.0442
Epoch 42/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.3047 - loss: 1.9853
[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3189 - loss: 2.0148 
[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3229 - loss: 2.0052
[1m 74/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3235 - loss: 2.0032
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3239 - loss: 2.0013
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3241 - loss: 1.9998
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3241 - loss: 1.9992 - val_accuracy: 0.3734 - val_loss: 2.0217
Epoch 43/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.3516 - loss: 1.9310
[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3277 - loss: 2.0045 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3265 - loss: 2.0000
[1m 74/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3258 - loss: 1.9980
[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3255 - loss: 1.9979
[1m122/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3258 - loss: 1.9970
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3260 - loss: 1.9961 - val_accuracy: 0.3713 - val_loss: 2.0248
Epoch 44/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.3438 - loss: 2.0941
[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3449 - loss: 1.9902 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3421 - loss: 1.9929
[1m 74/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3407 - loss: 1.9891
[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3393 - loss: 1.9868
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3385 - loss: 1.9850
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3378 - loss: 1.9849 - val_accuracy: 0.3742 - val_loss: 2.0035
Epoch 45/124

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Epoch 46/124

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Epoch 47/124

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Epoch 48/124

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[1m 48/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3373 - loss: 1.9645
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[1m 97/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3415 - loss: 1.9564
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Epoch 49/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3401 - loss: 1.9548 
[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3445 - loss: 1.9444
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[1m 95/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3455 - loss: 1.9420
[1m118/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3452 - loss: 1.9422
[1m144/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.3448 - loss: 1.9422
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Epoch 50/124

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[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3588 - loss: 1.9363 
[1m 48/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3538 - loss: 1.9379
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[1m 96/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3510 - loss: 1.9369
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Epoch 51/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3415 - loss: 1.9130 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3411 - loss: 1.9209
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[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3398 - loss: 1.9283
[1m129/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3401 - loss: 1.9289
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3405 - loss: 1.9289 - val_accuracy: 0.4064 - val_loss: 1.9786
Epoch 52/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.2812 - loss: 2.1107
[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3575 - loss: 1.9058 
[1m 48/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3595 - loss: 1.9019
[1m 73/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3571 - loss: 1.9051
[1m 96/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3559 - loss: 1.9071
[1m120/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3551 - loss: 1.9086
[1m143/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.3549 - loss: 1.9094
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3549 - loss: 1.9095 - val_accuracy: 0.3945 - val_loss: 2.0081
Epoch 53/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.3438 - loss: 1.8970
[1m 24/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3440 - loss: 1.9052 
[1m 47/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3474 - loss: 1.9078
[1m 72/145[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3481 - loss: 1.9110
[1m 97/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3494 - loss: 1.9107
[1m121/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3503 - loss: 1.9102
[1m142/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.3510 - loss: 1.9101
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3512 - loss: 1.9100 - val_accuracy: 0.4012 - val_loss: 1.9858
Epoch 54/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.3594 - loss: 1.9140
[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3611 - loss: 1.8783 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3594 - loss: 1.8814
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3579 - loss: 1.8881
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3566 - loss: 1.8926
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3558 - loss: 1.8949
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3554 - loss: 1.8964 - val_accuracy: 0.4006 - val_loss: 1.9796
Epoch 55/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.3516 - loss: 2.0126
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3535 - loss: 1.9106 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3551 - loss: 1.9014
[1m 75/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3565 - loss: 1.8979
[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3571 - loss: 1.8975
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3573 - loss: 1.8977
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3573 - loss: 1.8976 - val_accuracy: 0.4058 - val_loss: 1.9898
Epoch 56/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.3828 - loss: 1.8104
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3575 - loss: 1.8774 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3562 - loss: 1.8883
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3579 - loss: 1.8898
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3588 - loss: 1.8908
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3594 - loss: 1.8907
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3600 - loss: 1.8896 - val_accuracy: 0.4006 - val_loss: 1.9872

[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:14[0m 877ms/step
[1m58/86[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 884us/step  
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step 
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 11ms/step
Saved model to disk.

Accuracy capturado en la ejecución 16: 35.33 [%]
F1-score capturado en la ejecución 16: 34.66 [%]

=== EJECUCIÓN 17 ===

--- TRAIN (ejecución 17) ---

--- TEST (ejecución 17) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 409,231 (1.56 MB)
 Trainable params: 409,231 (1.56 MB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 6, 250)
(18469, 6, 250)

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[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 17ms/step
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[1m  1/158[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 17ms/step
[1m 59/158[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 866us/step
[1m117/158[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 870us/step
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 40.06 [%]
Global F1 score (validation) = 38.56 [%]
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[[0.00170307 0.00076927 0.00119425 ... 0.03258339 0.00262681 0.00055741]
 [0.00197399 0.00142495 0.00186753 ... 0.06685939 0.00374356 0.00054776]
 [0.00103147 0.00039576 0.0008415  ... 0.02551428 0.00177219 0.00038394]
 ...
 [0.16456537 0.05117587 0.21283475 ... 0.00047488 0.22471829 0.08413569]
 [0.16321415 0.10044408 0.14136155 ... 0.00437929 0.1368733  0.07745969]
 [0.18899032 0.04744791 0.19058058 ... 0.00045936 0.26106158 0.06335103]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 43.98 [%]
Global accuracy score (test) = 33.65 [%]
Global F1 score (train) = 43.54 [%]
Global F1 score (test) = 33.31 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.31      0.40      0.35       184
 CAMINAR CON MÓVIL O LIBRO       0.41      0.37      0.39       184
       CAMINAR USUAL SPEED       0.21      0.10      0.14       184
            CAMINAR ZIGZAG       0.15      0.15      0.15       184
          DE PIE BARRIENDO       0.27      0.21      0.23       184
   DE PIE DOBLANDO TOALLAS       0.32      0.30      0.31       184
    DE PIE MOVIENDO LIBROS       0.38      0.26      0.31       184
          DE PIE USANDO PC       0.27      0.26      0.26       184
        FASE REPOSO CON K5       0.41      0.74      0.53       184
INCREMENTAL CICLOERGOMETRO       0.53      0.63      0.58       184
           SENTADO LEYENDO       0.38      0.31      0.34       184
         SENTADO USANDO PC       0.13      0.08      0.10       184
      SENTADO VIENDO LA TV       0.43      0.40      0.41       184
   SUBIR Y BAJAR ESCALERAS       0.15      0.29      0.20       184
                    TROTAR       0.85      0.57      0.68       161

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

2025-10-28 12:59:18.315021: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-28 12:59:18.326354: 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 00:00:1761652758.344166 1854128 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761652758.348232 1854128 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761652758.358459 1854128 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761652758.358482 1854128 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761652758.358493 1854128 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761652758.358513 1854128 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-28 12:59:18.361573: 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 00:00:1761652760.711855 1854128 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13749 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 409,231 (1.56 MB)
 Trainable params: 409,231 (1.56 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/124
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761652763.274348 1854265 service.cc:152] XLA service 0x7e765c012200 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761652763.274423 1854265 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-28 12:59:23.332196: 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 00:00:1761652763.626707 1854265 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761652767.262305 1854265 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13:53[0m 6s/step - accuracy: 0.0781 - loss: 3.1780
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[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.0820 - loss: 3.1177
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 27ms/step - accuracy: 0.0830 - loss: 3.11162025-10-28 12:59:32.987177: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_316', 12 bytes spill stores, 12 bytes spill loads


[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m12s[0m 43ms/step - accuracy: 0.0831 - loss: 3.1114 - val_accuracy: 0.1858 - val_loss: 2.4760
Epoch 2/124

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[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1037 - loss: 2.9562
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Epoch 3/124

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Epoch 4/124

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Epoch 5/124

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Epoch 6/124

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[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1573 - loss: 2.6600
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Epoch 7/124

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[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1638 - loss: 2.6220
[1m129/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1652 - loss: 2.6176
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.1658 - loss: 2.6154 - val_accuracy: 0.2515 - val_loss: 2.2832
Epoch 8/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1789 - loss: 2.5766 
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[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1751 - loss: 2.5748
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Epoch 9/124

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[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1746 - loss: 2.5552
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1755 - loss: 2.5526
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Epoch 10/124

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[1m 48/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2007 - loss: 2.5087
[1m 73/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1975 - loss: 2.5079
[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1963 - loss: 2.5054
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1956 - loss: 2.5040
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.1953 - loss: 2.5034 - val_accuracy: 0.3014 - val_loss: 2.2210
Epoch 11/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.1797 - loss: 2.5325
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[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1965 - loss: 2.4702
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Epoch 12/124

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[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2008 - loss: 2.4501 
[1m 48/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2032 - loss: 2.4493
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[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2037 - loss: 2.4466
[1m123/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2040 - loss: 2.4445
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2041 - loss: 2.4432 - val_accuracy: 0.3302 - val_loss: 2.1824
Epoch 13/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2103 - loss: 2.4070 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2107 - loss: 2.4100
[1m 75/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2114 - loss: 2.4093
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2120 - loss: 2.4096
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2124 - loss: 2.4096
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2127 - loss: 2.4093 - val_accuracy: 0.3359 - val_loss: 2.1824
Epoch 14/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.2344 - loss: 2.2491
[1m 23/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2150 - loss: 2.3814 
[1m 48/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2130 - loss: 2.3819
[1m 72/145[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2132 - loss: 2.3816
[1m 97/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2147 - loss: 2.3798
[1m121/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2154 - loss: 2.3784
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.2160 - loss: 2.3776
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2160 - loss: 2.3776 - val_accuracy: 0.3351 - val_loss: 2.1581
Epoch 15/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2109 - loss: 2.3779
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2391 - loss: 2.3325 
[1m 53/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2358 - loss: 2.3392
[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2332 - loss: 2.3441
[1m104/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2315 - loss: 2.3474
[1m128/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2303 - loss: 2.3494
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2295 - loss: 2.3509 - val_accuracy: 0.3500 - val_loss: 2.1537
Epoch 16/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2812 - loss: 2.2888
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2422 - loss: 2.3475 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2432 - loss: 2.3453
[1m 75/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2417 - loss: 2.3456
[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2405 - loss: 2.3455
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2393 - loss: 2.3457
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2387 - loss: 2.3453 - val_accuracy: 0.3629 - val_loss: 2.1494
Epoch 17/124

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[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2222 - loss: 2.3304 
[1m 48/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2281 - loss: 2.3205
[1m 74/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2315 - loss: 2.3157
[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2329 - loss: 2.3135
[1m120/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2336 - loss: 2.3124
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Epoch 18/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2391 - loss: 2.3267 
[1m 48/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2355 - loss: 2.3230
[1m 75/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2367 - loss: 2.3152
[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2370 - loss: 2.3127
[1m127/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2375 - loss: 2.3103
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2380 - loss: 2.3087 - val_accuracy: 0.3576 - val_loss: 2.1308
Epoch 19/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2531 - loss: 2.2769 
[1m 53/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2487 - loss: 2.2863
[1m 79/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2472 - loss: 2.2900
[1m105/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2472 - loss: 2.2890
[1m130/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2479 - loss: 2.2865
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2482 - loss: 2.2852 - val_accuracy: 0.3655 - val_loss: 2.1178
Epoch 20/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2812 - loss: 2.2066
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2661 - loss: 2.2358 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2614 - loss: 2.2441
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2599 - loss: 2.2496
[1m104/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2592 - loss: 2.2530
[1m130/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2587 - loss: 2.2551
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2586 - loss: 2.2555 - val_accuracy: 0.3621 - val_loss: 2.1213
Epoch 21/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.3438 - loss: 2.1200
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2649 - loss: 2.2394 
[1m 53/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2639 - loss: 2.2440
[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2629 - loss: 2.2481
[1m105/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2627 - loss: 2.2489
[1m130/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2625 - loss: 2.2486
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2626 - loss: 2.2482 - val_accuracy: 0.3601 - val_loss: 2.1195
Epoch 22/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.2344 - loss: 2.2380
[1m 23/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2550 - loss: 2.2131 
[1m 47/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2592 - loss: 2.2162
[1m 71/145[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2604 - loss: 2.2203
[1m 95/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2609 - loss: 2.2226
[1m120/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2614 - loss: 2.2236
[1m144/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.2618 - loss: 2.2240
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2619 - loss: 2.2240 - val_accuracy: 0.3635 - val_loss: 2.1019
Epoch 23/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.2344 - loss: 2.3454
[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2559 - loss: 2.2257 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2621 - loss: 2.2127
[1m 72/145[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2647 - loss: 2.2103
[1m 96/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2661 - loss: 2.2097
[1m120/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2668 - loss: 2.2099
[1m144/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.2673 - loss: 2.2102
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Epoch 24/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 28ms/step - accuracy: 0.2344 - loss: 2.3185
[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2700 - loss: 2.2259 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2740 - loss: 2.2153
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2750 - loss: 2.2106
[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2744 - loss: 2.2087
[1m127/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2738 - loss: 2.2070
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2734 - loss: 2.2060 - val_accuracy: 0.3619 - val_loss: 2.0801
Epoch 25/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2891 - loss: 2.1551
[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2918 - loss: 2.1618 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2854 - loss: 2.1729
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2816 - loss: 2.1819
[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2797 - loss: 2.1850
[1m127/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2787 - loss: 2.1856
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2783 - loss: 2.1859 - val_accuracy: 0.3675 - val_loss: 2.0854
Epoch 26/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 28ms/step - accuracy: 0.3203 - loss: 2.3033
[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2814 - loss: 2.2056 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2808 - loss: 2.1946
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2808 - loss: 2.1900
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2805 - loss: 2.1883
[1m127/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2806 - loss: 2.1862
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2807 - loss: 2.1844 - val_accuracy: 0.3663 - val_loss: 2.0998
Epoch 27/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.3125 - loss: 2.2058
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2928 - loss: 2.1672 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2849 - loss: 2.1720
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2832 - loss: 2.1697
[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2826 - loss: 2.1680
[1m126/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2824 - loss: 2.1664
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2821 - loss: 2.1656 - val_accuracy: 0.3627 - val_loss: 2.0720
Epoch 28/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.3438 - loss: 2.1555
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3035 - loss: 2.1396 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2978 - loss: 2.1401
[1m 75/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2946 - loss: 2.1404
[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2932 - loss: 2.1403
[1m120/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2926 - loss: 2.1411
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2920 - loss: 2.1421 - val_accuracy: 0.3605 - val_loss: 2.0700
Epoch 29/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.2812 - loss: 2.1216
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2840 - loss: 2.1230 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2835 - loss: 2.1381
[1m 75/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2843 - loss: 2.1414
[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2847 - loss: 2.1424
[1m129/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2846 - loss: 2.1436
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2848 - loss: 2.1434 - val_accuracy: 0.3742 - val_loss: 2.0829
Epoch 30/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 27ms/step - accuracy: 0.2109 - loss: 2.4424
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2802 - loss: 2.1598 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2841 - loss: 2.1488
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2857 - loss: 2.1443
[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2870 - loss: 2.1404
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2881 - loss: 2.1377
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2890 - loss: 2.1355 - val_accuracy: 0.3615 - val_loss: 2.0565
Epoch 31/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2998 - loss: 2.1100 
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Epoch 32/124

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Epoch 33/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2929 - loss: 2.1033 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2958 - loss: 2.0993
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[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2964 - loss: 2.0968
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Epoch 34/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3047 - loss: 2.1035 
[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3038 - loss: 2.0983
[1m 72/145[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3028 - loss: 2.0962
[1m 96/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3023 - loss: 2.0954
[1m121/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3020 - loss: 2.0948
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3016 - loss: 2.0936 - val_accuracy: 0.3875 - val_loss: 2.0468
Epoch 35/124

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[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3081 - loss: 2.0725 
[1m 48/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3080 - loss: 2.0758
[1m 73/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3085 - loss: 2.0770
[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3083 - loss: 2.0779
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3089 - loss: 2.0771
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3092 - loss: 2.0763 - val_accuracy: 0.3814 - val_loss: 2.0411
Epoch 36/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.3281 - loss: 1.9979
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3288 - loss: 2.0253 
[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3216 - loss: 2.0363
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[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3178 - loss: 2.0437
[1m123/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3163 - loss: 2.0464
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3152 - loss: 2.0488 - val_accuracy: 0.3715 - val_loss: 2.0561
Epoch 37/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.3281 - loss: 1.9658
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3148 - loss: 2.0416 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3131 - loss: 2.0450
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3135 - loss: 2.0448
[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3139 - loss: 2.0445
[1m127/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3137 - loss: 2.0452
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3137 - loss: 2.0455 - val_accuracy: 0.3722 - val_loss: 2.0531
Epoch 38/124

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Epoch 39/124

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[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3192 - loss: 2.0342
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Epoch 40/124

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[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3150 - loss: 2.0443
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Epoch 41/124

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[1m 48/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3204 - loss: 2.0365
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[1m 95/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3219 - loss: 2.0263
[1m117/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3221 - loss: 2.0244
[1m143/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.3222 - loss: 2.0237
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3222 - loss: 2.0237 - val_accuracy: 0.3806 - val_loss: 2.0214
Epoch 42/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3176 - loss: 2.0133 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3224 - loss: 2.0052
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3246 - loss: 2.0047
[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3249 - loss: 2.0067
[1m127/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3251 - loss: 2.0079
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3254 - loss: 2.0081 - val_accuracy: 0.3790 - val_loss: 2.0220
Epoch 43/124

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[1m 24/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3302 - loss: 1.9962 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3290 - loss: 2.0002
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[1m 97/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3293 - loss: 1.9968
[1m121/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3287 - loss: 1.9965
[1m144/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.3283 - loss: 1.9964
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3283 - loss: 1.9964 - val_accuracy: 0.3885 - val_loss: 2.0065
Epoch 44/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3326 - loss: 1.9904 
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[1m 73/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3320 - loss: 1.9936
[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3314 - loss: 1.9939
[1m122/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3311 - loss: 1.9941
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Epoch 45/124

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[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3363 - loss: 1.9638 
[1m 48/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3321 - loss: 1.9724
[1m 73/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3296 - loss: 1.9780
[1m 97/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3288 - loss: 1.9805
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Epoch 46/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3302 - loss: 1.9914 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3310 - loss: 1.9857
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3313 - loss: 1.9839
[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3316 - loss: 1.9821
[1m126/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3315 - loss: 1.9812
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3314 - loss: 1.9809 - val_accuracy: 0.3913 - val_loss: 2.0038
Epoch 47/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.3125 - loss: 1.9683
[1m 24/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3319 - loss: 1.9507 
[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3295 - loss: 1.9624
[1m 74/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3306 - loss: 1.9647
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3315 - loss: 1.9639
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3325 - loss: 1.9632
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3329 - loss: 1.9629 - val_accuracy: 0.3842 - val_loss: 2.0109
Epoch 48/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2812 - loss: 2.0359
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3359 - loss: 1.9562 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3353 - loss: 1.9636
[1m 75/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3348 - loss: 1.9669
[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3350 - loss: 1.9667
[1m127/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3353 - loss: 1.9668
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3358 - loss: 1.9660 - val_accuracy: 0.3927 - val_loss: 1.9911
Epoch 49/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.3047 - loss: 2.0597
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3471 - loss: 1.9224 
[1m 53/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3472 - loss: 1.9340
[1m 80/145[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3468 - loss: 1.9387
[1m107/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3469 - loss: 1.9394
[1m130/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3463 - loss: 1.9406
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3459 - loss: 1.9416 - val_accuracy: 0.3853 - val_loss: 1.9977
Epoch 50/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.4688 - loss: 1.8398
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3638 - loss: 1.9001 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3581 - loss: 1.9077
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3554 - loss: 1.9134
[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3541 - loss: 1.9183
[1m129/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3533 - loss: 1.9218
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3529 - loss: 1.9235 - val_accuracy: 0.3969 - val_loss: 2.0099
Epoch 51/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.3516 - loss: 1.8863
[1m 24/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3438 - loss: 1.9436 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3475 - loss: 1.9308
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3486 - loss: 1.9273
[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3487 - loss: 1.9276
[1m126/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3490 - loss: 1.9275
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3491 - loss: 1.9276 - val_accuracy: 0.3838 - val_loss: 1.9938
Epoch 52/124

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Epoch 53/124

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[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3610 - loss: 1.8909
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Epoch 54/124

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[1m 48/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3551 - loss: 1.8967
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[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3548 - loss: 1.9024
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Epoch 55/124

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[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3473 - loss: 1.9145 
[1m 48/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3527 - loss: 1.9002
[1m 73/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3545 - loss: 1.8951
[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3556 - loss: 1.8927
[1m123/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3562 - loss: 1.8926
[1m144/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.3566 - loss: 1.8929
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Epoch 56/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.3203 - loss: 1.9242
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3401 - loss: 1.9023 
[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3490 - loss: 1.8996
[1m 72/145[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3533 - loss: 1.8995
[1m 95/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3546 - loss: 1.9002
[1m120/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3558 - loss: 1.8986
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3566 - loss: 1.8969 - val_accuracy: 0.3990 - val_loss: 1.9823
Epoch 57/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3667 - loss: 1.8385 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3623 - loss: 1.8565
[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3626 - loss: 1.8632
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3625 - loss: 1.8663
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3621 - loss: 1.8696
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3617 - loss: 1.8720 - val_accuracy: 0.3988 - val_loss: 1.9776
Epoch 58/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3392 - loss: 1.9217 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3499 - loss: 1.9044
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[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3569 - loss: 1.8893
[1m129/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3584 - loss: 1.8856
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Epoch 59/124

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Epoch 60/124

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Epoch 61/124

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[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3776 - loss: 1.8505
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Epoch 62/124

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[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3720 - loss: 1.8422
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3725 - loss: 1.8427
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Epoch 63/124

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[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3722 - loss: 1.8379
[1m127/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3725 - loss: 1.8395
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Epoch 64/124

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[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3753 - loss: 1.8206
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Epoch 65/124

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[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3858 - loss: 1.8157
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Epoch 66/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4009 - loss: 1.8429 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3974 - loss: 1.8294
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3941 - loss: 1.8253
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3921 - loss: 1.8240
[1m127/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3907 - loss: 1.8228
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3898 - loss: 1.8223 - val_accuracy: 0.3996 - val_loss: 1.9955
Epoch 67/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.3984 - loss: 1.6845
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[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3906 - loss: 1.8044
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3896 - loss: 1.8110
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3890 - loss: 1.8143
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3888 - loss: 1.8158
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3886 - loss: 1.8164 - val_accuracy: 0.4014 - val_loss: 2.0014
Epoch 68/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.3828 - loss: 1.8578
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3885 - loss: 1.8261 
[1m 54/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3909 - loss: 1.8143
[1m 79/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3918 - loss: 1.8094
[1m104/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3921 - loss: 1.8094
[1m130/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3921 - loss: 1.8095
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3921 - loss: 1.8093 - val_accuracy: 0.4036 - val_loss: 1.9685

[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:11[0m 844ms/step
[1m56/86[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 920us/step  
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step 
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 11ms/step
Saved model to disk.

Accuracy capturado en la ejecución 17: 33.65 [%]
F1-score capturado en la ejecución 17: 33.31 [%]

=== EJECUCIÓN 18 ===

--- TRAIN (ejecución 18) ---

--- TEST (ejecución 18) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 409,231 (1.56 MB)
 Trainable params: 409,231 (1.56 MB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 6, 250)
(18469, 6, 250)

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[1m 55/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 939us/step
[1m121/578[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 843us/step
[1m177/578[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 860us/step
[1m243/578[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 834us/step
[1m304/578[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 833us/step
[1m372/578[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 816us/step
[1m430/578[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 823us/step
[1m492/578[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 821us/step
[1m559/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 813us/step
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 2ms/step

[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m56/86[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 914us/step
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step  
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 10ms/step

[1m  1/158[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 17ms/step
[1m 62/158[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 829us/step
[1m124/158[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 822us/step
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 40.36 [%]
Global F1 score (validation) = 38.76 [%]
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[[1.6974353e-03 8.0112455e-04 2.3663214e-03 ... 3.3850409e-02
  2.9062813e-03 1.0434418e-03]
 [1.0989582e-03 6.3277798e-04 1.9118340e-03 ... 5.0845955e-02
  2.1282160e-03 6.9738872e-04]
 [1.0865241e-03 2.7119202e-04 1.4551567e-03 ... 2.8115701e-02
  1.8743317e-03 9.7210862e-04]
 ...
 [1.2841643e-01 2.8307416e-02 2.3667222e-01 ... 1.9116073e-04
  2.4305643e-01 1.0788498e-01]
 [2.2510421e-01 1.2705547e-01 1.6461717e-01 ... 7.3575182e-04
  1.8919735e-01 4.6633109e-02]
 [2.2566560e-01 3.4051478e-02 1.6232656e-01 ... 5.1120960e-04
  2.6628396e-01 5.1822904e-02]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 48.13 [%]
Global accuracy score (test) = 34.34 [%]
Global F1 score (train) = 48.13 [%]
Global F1 score (test) = 33.35 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.30      0.52      0.38       184
 CAMINAR CON MÓVIL O LIBRO       0.36      0.32      0.34       184
       CAMINAR USUAL SPEED       0.14      0.08      0.10       184
            CAMINAR ZIGZAG       0.14      0.09      0.11       184
          DE PIE BARRIENDO       0.36      0.31      0.33       184
   DE PIE DOBLANDO TOALLAS       0.37      0.32      0.34       184
    DE PIE MOVIENDO LIBROS       0.48      0.29      0.36       184
          DE PIE USANDO PC       0.23      0.26      0.24       184
        FASE REPOSO CON K5       0.44      0.74      0.55       184
INCREMENTAL CICLOERGOMETRO       0.59      0.64      0.61       184
           SENTADO LEYENDO       0.38      0.31      0.34       184
         SENTADO USANDO PC       0.14      0.11      0.12       184
      SENTADO VIENDO LA TV       0.35      0.38      0.36       184
   SUBIR Y BAJAR ESCALERAS       0.17      0.22      0.19       184
                    TROTAR       0.62      0.60      0.61       161

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

2025-10-28 13:00:15.409302: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-28 13:00:15.420727: 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 00:00:1761652815.434531 1861531 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761652815.438948 1861531 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761652815.449406 1861531 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761652815.449432 1861531 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761652815.449435 1861531 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761652815.449438 1861531 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-28 13:00:15.452738: 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 00:00:1761652817.793254 1861531 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13749 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 409,231 (1.56 MB)
 Trainable params: 409,231 (1.56 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/124
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761652820.346246 1861644 service.cc:152] XLA service 0x7513000248c0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761652820.346300 1861644 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-28 13:00:20.400150: 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 00:00:1761652820.701424 1861644 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761652824.328030 1861644 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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
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Epoch 2/124

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Epoch 3/124

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Epoch 4/124

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Epoch 5/124

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Epoch 6/124

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Epoch 7/124

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[1m105/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1607 - loss: 2.6232
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Epoch 8/124

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[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1614 - loss: 2.6051
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Epoch 9/124

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[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1799 - loss: 2.5602 
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[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1798 - loss: 2.5484
[1m127/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1797 - loss: 2.5460
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.1799 - loss: 2.5441 - val_accuracy: 0.2720 - val_loss: 2.3019
Epoch 10/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1792 - loss: 2.5398 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1837 - loss: 2.5245
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1847 - loss: 2.5195
[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1858 - loss: 2.5156
[1m129/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1868 - loss: 2.5128
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.1874 - loss: 2.5110 - val_accuracy: 0.2885 - val_loss: 2.2850
Epoch 11/124

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[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2011 - loss: 2.4496 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2008 - loss: 2.4548
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[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1980 - loss: 2.4595
[1m127/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1972 - loss: 2.4613
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.1966 - loss: 2.4623 - val_accuracy: 0.2988 - val_loss: 2.2461
Epoch 12/124

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[1m104/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2040 - loss: 2.4386
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Epoch 13/124

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[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2045 - loss: 2.4085
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Epoch 14/124

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[1m 96/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2159 - loss: 2.3920
[1m119/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2162 - loss: 2.3910
[1m143/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.2165 - loss: 2.3900
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2165 - loss: 2.3899 - val_accuracy: 0.3290 - val_loss: 2.2432
Epoch 15/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2734 - loss: 2.2458
[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2412 - loss: 2.3678 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2328 - loss: 2.3776
[1m 75/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2292 - loss: 2.3769
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2271 - loss: 2.3752
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2261 - loss: 2.3727
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2257 - loss: 2.3706 - val_accuracy: 0.3353 - val_loss: 2.1998
Epoch 16/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2031 - loss: 2.3886
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2210 - loss: 2.3431 
[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2253 - loss: 2.3436
[1m 73/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2272 - loss: 2.3426
[1m 95/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2281 - loss: 2.3423
[1m118/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2283 - loss: 2.3417
[1m143/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.2285 - loss: 2.3407
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2286 - loss: 2.3406 - val_accuracy: 0.3462 - val_loss: 2.2110
Epoch 17/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.1797 - loss: 2.3439
[1m 22/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2183 - loss: 2.3429 
[1m 46/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2232 - loss: 2.3414
[1m 71/145[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2253 - loss: 2.3409
[1m 95/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2265 - loss: 2.3397
[1m120/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2275 - loss: 2.3376
[1m143/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.2286 - loss: 2.3347
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2287 - loss: 2.3344 - val_accuracy: 0.3415 - val_loss: 2.2089
Epoch 18/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2969 - loss: 2.2770
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2462 - loss: 2.3109 
[1m 54/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2440 - loss: 2.3132
[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2435 - loss: 2.3119
[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2434 - loss: 2.3110
[1m128/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2436 - loss: 2.3093
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Epoch 19/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2969 - loss: 2.2491
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2353 - loss: 2.2943 
[1m 53/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2373 - loss: 2.2919
[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2392 - loss: 2.2895
[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2403 - loss: 2.2883
[1m126/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2411 - loss: 2.2877
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2417 - loss: 2.2867 - val_accuracy: 0.3534 - val_loss: 2.1744
Epoch 20/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2500 - loss: 2.3531
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2584 - loss: 2.2655 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2564 - loss: 2.2643
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[1m 95/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2545 - loss: 2.2643
[1m119/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2541 - loss: 2.2637
[1m143/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.2541 - loss: 2.2633
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2541 - loss: 2.2633 - val_accuracy: 0.3435 - val_loss: 2.1838
Epoch 21/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.2578 - loss: 2.2298
[1m 23/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2561 - loss: 2.2393 
[1m 47/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2542 - loss: 2.2408
[1m 70/145[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2546 - loss: 2.2411
[1m 92/145[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2547 - loss: 2.2423
[1m118/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2550 - loss: 2.2436
[1m142/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.2557 - loss: 2.2438
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2558 - loss: 2.2438 - val_accuracy: 0.3488 - val_loss: 2.2069
Epoch 22/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2422 - loss: 2.2807
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2588 - loss: 2.2561 
[1m 54/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2588 - loss: 2.2443
[1m 79/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2601 - loss: 2.2368
[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2608 - loss: 2.2332
[1m128/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2613 - loss: 2.2306
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2616 - loss: 2.2297 - val_accuracy: 0.3512 - val_loss: 2.1746
Epoch 23/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2578 - loss: 2.1249
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2728 - loss: 2.1844 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2693 - loss: 2.1961
[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2685 - loss: 2.2005
[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2681 - loss: 2.2028
[1m127/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2681 - loss: 2.2044
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2683 - loss: 2.2046 - val_accuracy: 0.3510 - val_loss: 2.1624
Epoch 24/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.3594 - loss: 2.0622
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2958 - loss: 2.1686 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2858 - loss: 2.1836
[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2818 - loss: 2.1889
[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2799 - loss: 2.1919
[1m127/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2786 - loss: 2.1931
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2779 - loss: 2.1939 - val_accuracy: 0.3583 - val_loss: 2.1434
Epoch 25/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.2891 - loss: 2.2483
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2612 - loss: 2.2142 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2602 - loss: 2.2161
[1m 73/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2624 - loss: 2.2110
[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2646 - loss: 2.2039
[1m122/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2658 - loss: 2.1990
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2668 - loss: 2.1960 - val_accuracy: 0.3560 - val_loss: 2.1344
Epoch 26/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.2656 - loss: 2.1561
[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2747 - loss: 2.1718 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2751 - loss: 2.1669
[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2759 - loss: 2.1655
[1m104/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2768 - loss: 2.1643
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Epoch 27/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2859 - loss: 2.1489 
[1m 53/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2839 - loss: 2.1549
[1m 80/145[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2834 - loss: 2.1581
[1m104/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2834 - loss: 2.1588
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Epoch 28/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2852 - loss: 2.1637 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2858 - loss: 2.1542
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2886 - loss: 2.1468
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2897 - loss: 2.1437
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2898 - loss: 2.1427
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2895 - loss: 2.1427 - val_accuracy: 0.3611 - val_loss: 2.1155
Epoch 29/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.3047 - loss: 2.0733
[1m 24/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3013 - loss: 2.0927 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2975 - loss: 2.1068
[1m 73/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2971 - loss: 2.1105
[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2948 - loss: 2.1153
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2931 - loss: 2.1183
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2922 - loss: 2.1199 - val_accuracy: 0.3627 - val_loss: 2.1266
Epoch 30/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.3203 - loss: 2.1167
[1m 24/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2914 - loss: 2.1259 
[1m 47/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2872 - loss: 2.1277
[1m 70/145[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2872 - loss: 2.1259
[1m 94/145[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2876 - loss: 2.1250
[1m119/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2878 - loss: 2.1246
[1m142/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.2879 - loss: 2.1242
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2879 - loss: 2.1242 - val_accuracy: 0.3625 - val_loss: 2.1349
Epoch 31/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2969 - loss: 1.9448
[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2889 - loss: 2.0802 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2904 - loss: 2.0940
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2901 - loss: 2.1006
[1m104/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2907 - loss: 2.1033
[1m130/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2910 - loss: 2.1046
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2912 - loss: 2.1051 - val_accuracy: 0.3597 - val_loss: 2.1228
Epoch 32/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3074 - loss: 2.0902 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3043 - loss: 2.0882
[1m 75/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3033 - loss: 2.0913
[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3023 - loss: 2.0937
[1m129/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3013 - loss: 2.0948
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3008 - loss: 2.0954 - val_accuracy: 0.3625 - val_loss: 2.1183
Epoch 33/124

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Epoch 34/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3071 - loss: 2.0596 
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[1m 97/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3034 - loss: 2.0677
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Epoch 35/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2905 - loss: 2.0909 
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[1m 79/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2961 - loss: 2.0778
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Epoch 36/124

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[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3218 - loss: 2.0282 
[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3135 - loss: 2.0467
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[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3111 - loss: 2.0522
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Epoch 37/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3343 - loss: 1.9894 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3290 - loss: 2.0067
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3259 - loss: 2.0169
[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3238 - loss: 2.0229
[1m126/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3219 - loss: 2.0277
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3209 - loss: 2.0304 - val_accuracy: 0.3722 - val_loss: 2.0759
Epoch 38/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3112 - loss: 2.0607 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3125 - loss: 2.0612
[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3121 - loss: 2.0617
[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3128 - loss: 2.0586
[1m126/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3133 - loss: 2.0556
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3136 - loss: 2.0534 - val_accuracy: 0.3651 - val_loss: 2.0803
Epoch 39/124

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[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3302 - loss: 2.0212 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3256 - loss: 2.0218
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[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3212 - loss: 2.0268
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3202 - loss: 2.0272
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Epoch 40/124

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Epoch 41/124

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[1m 23/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3204 - loss: 2.0334 
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Epoch 42/124

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[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3252 - loss: 2.0036
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Epoch 43/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3448 - loss: 1.9798 
[1m 54/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3383 - loss: 1.9859
[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3358 - loss: 1.9892
[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3342 - loss: 1.9915
[1m129/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3336 - loss: 1.9918
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Epoch 44/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3223 - loss: 1.9832 
[1m 54/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3285 - loss: 1.9761
[1m 79/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3305 - loss: 1.9741
[1m105/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3305 - loss: 1.9732
[1m129/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3304 - loss: 1.9727
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3304 - loss: 1.9727 - val_accuracy: 0.3861 - val_loss: 2.0527
Epoch 45/124

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[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3377 - loss: 1.9369 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3358 - loss: 1.9527
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[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3349 - loss: 1.9582
[1m126/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3348 - loss: 1.9596
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3347 - loss: 1.9606 - val_accuracy: 0.3937 - val_loss: 2.0345
Epoch 46/124

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[1m 47/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3301 - loss: 1.9670
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[1m 93/145[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3332 - loss: 1.9661
[1m118/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3337 - loss: 1.9656
[1m143/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.3344 - loss: 1.9649
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Epoch 47/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3457 - loss: 1.9359 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3441 - loss: 1.9464
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3412 - loss: 1.9539
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3397 - loss: 1.9578
[1m126/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3391 - loss: 1.9590
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3391 - loss: 1.9591 - val_accuracy: 0.3979 - val_loss: 2.0343
Epoch 48/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2969 - loss: 2.1195
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3304 - loss: 1.9728 
[1m 54/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3388 - loss: 1.9541
[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3413 - loss: 1.9508
[1m104/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3425 - loss: 1.9490
[1m128/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3432 - loss: 1.9487
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3436 - loss: 1.9483 - val_accuracy: 0.3861 - val_loss: 2.0589
Epoch 49/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.2656 - loss: 2.0854
[1m 24/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3300 - loss: 1.9602 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3353 - loss: 1.9569
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3362 - loss: 1.9570
[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3374 - loss: 1.9559
[1m126/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3389 - loss: 1.9538
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3396 - loss: 1.9522 - val_accuracy: 0.3990 - val_loss: 2.0416
Epoch 50/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.3047 - loss: 1.9084
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3306 - loss: 1.9531 
[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3334 - loss: 1.9520
[1m 75/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3352 - loss: 1.9513
[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3364 - loss: 1.9502
[1m126/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3376 - loss: 1.9492
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3385 - loss: 1.9478 - val_accuracy: 0.3977 - val_loss: 2.0354
Epoch 51/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.3203 - loss: 2.0974
[1m 24/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3370 - loss: 1.9502 
[1m 48/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3390 - loss: 1.9379
[1m 73/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3409 - loss: 1.9310
[1m 97/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3424 - loss: 1.9277
[1m120/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3434 - loss: 1.9261
[1m144/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.3439 - loss: 1.9256
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3439 - loss: 1.9255 - val_accuracy: 0.3939 - val_loss: 2.0595
Epoch 52/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.3516 - loss: 1.9143
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3527 - loss: 1.9174 
[1m 53/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3523 - loss: 1.9188
[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3511 - loss: 1.9212
[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3510 - loss: 1.9215
[1m129/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3509 - loss: 1.9207
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3509 - loss: 1.9202 - val_accuracy: 0.4102 - val_loss: 2.0578

[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:13[0m 860ms/step
[1m58/86[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 878us/step  
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step 
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 10ms/step
Saved model to disk.

Accuracy capturado en la ejecución 18: 34.34 [%]
F1-score capturado en la ejecución 18: 33.35 [%]

=== EJECUCIÓN 19 ===

--- TRAIN (ejecución 19) ---

--- TEST (ejecución 19) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 409,231 (1.56 MB)
 Trainable params: 409,231 (1.56 MB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 6, 250)
(18469, 6, 250)

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[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
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[1m  1/158[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step
[1m 59/158[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 867us/step
[1m117/158[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 872us/step
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 6ms/step
Global accuracy score (validation) = 41.02 [%]
Global F1 score (validation) = 38.77 [%]
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[[0.00226221 0.00144106 0.00220547 ... 0.03646129 0.00373886 0.00081962]
 [0.00269504 0.00223177 0.00316283 ... 0.09706333 0.00487346 0.00131891]
 [0.00116053 0.0003855  0.0013261  ... 0.02079699 0.00223157 0.00045296]
 ...
 [0.20164473 0.0469249  0.19636187 ... 0.0010113  0.21184473 0.0770965 ]
 [0.20456597 0.06554736 0.199484   ... 0.00095422 0.20773542 0.06961716]
 [0.2228135  0.05080012 0.19494258 ... 0.00105101 0.22383472 0.05607046]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 42.8 [%]
Global accuracy score (test) = 35.26 [%]
Global F1 score (train) = 42.23 [%]
Global F1 score (test) = 34.29 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.31      0.48      0.38       184
 CAMINAR CON MÓVIL O LIBRO       0.38      0.36      0.37       184
       CAMINAR USUAL SPEED       0.08      0.03      0.05       184
            CAMINAR ZIGZAG       0.20      0.34      0.25       184
          DE PIE BARRIENDO       0.25      0.23      0.24       184
   DE PIE DOBLANDO TOALLAS       0.32      0.39      0.35       184
    DE PIE MOVIENDO LIBROS       0.40      0.19      0.26       184
          DE PIE USANDO PC       0.25      0.21      0.23       184
        FASE REPOSO CON K5       0.48      0.74      0.58       184
INCREMENTAL CICLOERGOMETRO       0.51      0.61      0.56       184
           SENTADO LEYENDO       0.44      0.38      0.41       184
         SENTADO USANDO PC       0.15      0.11      0.13       184
      SENTADO VIENDO LA TV       0.54      0.47      0.50       184
   SUBIR Y BAJAR ESCALERAS       0.23      0.21      0.22       184
                    TROTAR       0.71      0.57      0.63       161

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

2025-10-28 13:01:05.648289: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-28 13:01:05.659688: 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 00:00:1761652865.672827 1867410 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761652865.677096 1867410 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761652865.687125 1867410 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761652865.687149 1867410 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761652865.687152 1867410 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761652865.687155 1867410 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-28 13:01:05.690417: 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 00:00:1761652868.069539 1867410 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13749 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 409,231 (1.56 MB)
 Trainable params: 409,231 (1.56 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/124
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761652870.642641 1867540 service.cc:152] XLA service 0x70d2d8026960 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761652870.642681 1867540 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-28 13:01:10.695206: 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 00:00:1761652870.995348 1867540 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761652874.563502 1867540 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13:45[0m 6s/step - accuracy: 0.0938 - loss: 3.1569
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[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.0675 - loss: 3.2199
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[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.0723 - loss: 3.1847
[1m127/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.0743 - loss: 3.1720
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 27ms/step - accuracy: 0.0755 - loss: 3.16412025-10-28 13:01:20.235999: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_316', 12 bytes spill stores, 12 bytes spill loads


[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m12s[0m 43ms/step - accuracy: 0.0756 - loss: 3.1637 - val_accuracy: 0.1832 - val_loss: 2.4881
Epoch 2/124

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Epoch 3/124

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Epoch 4/124

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Epoch 5/124

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[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1449 - loss: 2.7188
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Epoch 6/124

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[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1536 - loss: 2.6512
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Epoch 7/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1577 - loss: 2.6026 
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[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1583 - loss: 2.6116
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[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.1600 - loss: 2.6101 - val_accuracy: 0.2619 - val_loss: 2.3239
Epoch 8/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1627 - loss: 2.5812 
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Epoch 9/124

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[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1701 - loss: 2.5501
[1m122/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1707 - loss: 2.5493
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.1712 - loss: 2.5484 - val_accuracy: 0.2883 - val_loss: 2.3019
Epoch 10/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.2188 - loss: 2.4349
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[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1873 - loss: 2.4881
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1873 - loss: 2.4897
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.1872 - loss: 2.4909 - val_accuracy: 0.3063 - val_loss: 2.2617
Epoch 11/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.2188 - loss: 2.3611
[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1876 - loss: 2.4818 
[1m 48/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1897 - loss: 2.4790
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[1m 95/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1897 - loss: 2.4750
[1m120/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1902 - loss: 2.4734
[1m144/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.1908 - loss: 2.4724
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.1908 - loss: 2.4723 - val_accuracy: 0.3117 - val_loss: 2.2423
Epoch 12/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.1641 - loss: 2.4232
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1983 - loss: 2.4241 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2022 - loss: 2.4296
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2040 - loss: 2.4316
[1m104/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2055 - loss: 2.4324
[1m128/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2059 - loss: 2.4329
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2062 - loss: 2.4332 - val_accuracy: 0.3294 - val_loss: 2.2186
Epoch 13/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.1562 - loss: 2.4142
[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1942 - loss: 2.4259 
[1m 48/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1984 - loss: 2.4223
[1m 73/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2010 - loss: 2.4178
[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2023 - loss: 2.4153
[1m123/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2028 - loss: 2.4143
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2034 - loss: 2.4133 - val_accuracy: 0.3359 - val_loss: 2.2101
Epoch 14/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2031 - loss: 2.4373
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2231 - loss: 2.3921 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2205 - loss: 2.3906
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2199 - loss: 2.3890
[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2198 - loss: 2.3871
[1m129/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2195 - loss: 2.3861
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2192 - loss: 2.3859 - val_accuracy: 0.3379 - val_loss: 2.2057
Epoch 15/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.2188 - loss: 2.4676
[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2168 - loss: 2.3960 
[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2201 - loss: 2.3876
[1m 73/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2209 - loss: 2.3824
[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2213 - loss: 2.3797
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2219 - loss: 2.3764
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2225 - loss: 2.3742 - val_accuracy: 0.3490 - val_loss: 2.1929
Epoch 16/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.1953 - loss: 2.2923
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2281 - loss: 2.3300 
[1m 54/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2298 - loss: 2.3368
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[1m105/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2301 - loss: 2.3368
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Epoch 17/124

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[1m 96/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2386 - loss: 2.3174
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Epoch 18/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2406 - loss: 2.3090 
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[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2420 - loss: 2.3074
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2426 - loss: 2.3050
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Epoch 19/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2421 - loss: 2.3029 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2457 - loss: 2.3018
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2469 - loss: 2.2989
[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2472 - loss: 2.2958
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2472 - loss: 2.2936
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2473 - loss: 2.2918 - val_accuracy: 0.3627 - val_loss: 2.1553
Epoch 20/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.2891 - loss: 2.2843
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2448 - loss: 2.2807 
[1m 53/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2484 - loss: 2.2710
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2486 - loss: 2.2685
[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2476 - loss: 2.2703
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2475 - loss: 2.2707
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2477 - loss: 2.2704 - val_accuracy: 0.3500 - val_loss: 2.1652
Epoch 21/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.2109 - loss: 2.2965
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2566 - loss: 2.2544 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2580 - loss: 2.2510
[1m 74/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2584 - loss: 2.2492
[1m 96/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2581 - loss: 2.2487
[1m122/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2581 - loss: 2.2476
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2582 - loss: 2.2471 - val_accuracy: 0.3649 - val_loss: 2.1441
Epoch 22/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2422 - loss: 2.2639
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2485 - loss: 2.2469 
[1m 53/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2518 - loss: 2.2467
[1m 80/145[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2525 - loss: 2.2460
[1m104/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2528 - loss: 2.2455
[1m126/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2530 - loss: 2.2453
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2537 - loss: 2.2437 - val_accuracy: 0.3639 - val_loss: 2.1641
Epoch 23/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2812 - loss: 2.2495
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2796 - loss: 2.1865 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2726 - loss: 2.1939
[1m 74/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2703 - loss: 2.1994
[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2692 - loss: 2.2039
[1m123/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2686 - loss: 2.2067
[1m144/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.2681 - loss: 2.2085
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2681 - loss: 2.2086 - val_accuracy: 0.3538 - val_loss: 2.1518
Epoch 24/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.2500 - loss: 2.3510
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2656 - loss: 2.2087 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2656 - loss: 2.2069
[1m 74/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2650 - loss: 2.2097
[1m 97/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2651 - loss: 2.2112
[1m122/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2658 - loss: 2.2103
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2663 - loss: 2.2092 - val_accuracy: 0.3623 - val_loss: 2.1461
Epoch 25/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2422 - loss: 2.2889
[1m 24/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2620 - loss: 2.2229 
[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2630 - loss: 2.2172
[1m 73/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2651 - loss: 2.2115
[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2668 - loss: 2.2073
[1m123/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2680 - loss: 2.2040
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2689 - loss: 2.2019 - val_accuracy: 0.3498 - val_loss: 2.1336
Epoch 26/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.2891 - loss: 2.1353
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2787 - loss: 2.1812 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2753 - loss: 2.1862
[1m 75/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2740 - loss: 2.1876
[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2731 - loss: 2.1878
[1m127/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2735 - loss: 2.1858
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2736 - loss: 2.1854 - val_accuracy: 0.3548 - val_loss: 2.1229
Epoch 27/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2656 - loss: 2.2689
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2881 - loss: 2.1804 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2847 - loss: 2.1754
[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2829 - loss: 2.1726
[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2814 - loss: 2.1716
[1m127/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2806 - loss: 2.1701
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2802 - loss: 2.1696 - val_accuracy: 0.3623 - val_loss: 2.1207
Epoch 28/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2344 - loss: 2.2506
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2709 - loss: 2.1657 
[1m 54/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2765 - loss: 2.1600
[1m 79/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2783 - loss: 2.1569
[1m105/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2791 - loss: 2.1551
[1m129/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2792 - loss: 2.1543
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2793 - loss: 2.1537 - val_accuracy: 0.3625 - val_loss: 2.0977
Epoch 29/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.2656 - loss: 2.2363
[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2864 - loss: 2.1594 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2882 - loss: 2.1490
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2884 - loss: 2.1449
[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2887 - loss: 2.1430
[1m123/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2886 - loss: 2.1419
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2885 - loss: 2.1404 - val_accuracy: 0.3641 - val_loss: 2.1161
Epoch 30/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2883 - loss: 2.1414 
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Epoch 31/124

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Epoch 32/124

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[1m 97/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3046 - loss: 2.1071
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Epoch 33/124

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[1m 54/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2877 - loss: 2.1272
[1m 79/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2921 - loss: 2.1175
[1m105/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2940 - loss: 2.1128
[1m130/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2950 - loss: 2.1099
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2957 - loss: 2.1080 - val_accuracy: 0.3752 - val_loss: 2.0714
Epoch 34/124

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[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2964 - loss: 2.0887 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2990 - loss: 2.0859
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2996 - loss: 2.0844
[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3003 - loss: 2.0832
[1m126/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3010 - loss: 2.0819
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3011 - loss: 2.0817 - val_accuracy: 0.3689 - val_loss: 2.0856
Epoch 35/124

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[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3000 - loss: 2.0694 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2958 - loss: 2.0775
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[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2962 - loss: 2.0838
[1m128/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2971 - loss: 2.0839
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2977 - loss: 2.0832 - val_accuracy: 0.3677 - val_loss: 2.0819
Epoch 36/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.3828 - loss: 1.8865
[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3232 - loss: 2.0226 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3160 - loss: 2.0352
[1m 74/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3141 - loss: 2.0402
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3133 - loss: 2.0441
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3130 - loss: 2.0466
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3128 - loss: 2.0484 - val_accuracy: 0.3722 - val_loss: 2.0560
Epoch 37/124

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Epoch 38/124

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Epoch 39/124

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[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3217 - loss: 2.0217
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Epoch 40/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3320 - loss: 2.0053 
[1m 53/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3298 - loss: 2.0081
[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3268 - loss: 2.0138
[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3256 - loss: 2.0167
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3249 - loss: 2.0179
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3241 - loss: 2.0193 - val_accuracy: 0.3790 - val_loss: 2.0407
Epoch 41/124

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[1m 28/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3187 - loss: 1.9959 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3215 - loss: 1.9920
[1m 75/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3236 - loss: 1.9902
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3245 - loss: 1.9918
[1m127/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3245 - loss: 1.9948
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3245 - loss: 1.9964 - val_accuracy: 0.3909 - val_loss: 2.0347
Epoch 42/124

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[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3307 - loss: 2.0018 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3285 - loss: 2.0057
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[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3264 - loss: 2.0069
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3258 - loss: 2.0069
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3256 - loss: 2.0064 - val_accuracy: 0.3931 - val_loss: 2.0272
Epoch 43/124

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[1m 24/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3342 - loss: 1.9969 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3356 - loss: 1.9905
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[1m 95/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3341 - loss: 1.9894
[1m121/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3336 - loss: 1.9904
[1m143/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.3333 - loss: 1.9909
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Epoch 44/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.3984 - loss: 2.0025
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[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3258 - loss: 2.0000
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Epoch 45/124

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[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3302 - loss: 1.9496
[1m105/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3303 - loss: 1.9566
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Epoch 46/124

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[1m 24/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3355 - loss: 1.9834 
[1m 48/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3325 - loss: 1.9829
[1m 72/145[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3322 - loss: 1.9827
[1m 95/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3336 - loss: 1.9793
[1m118/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3345 - loss: 1.9771
[1m140/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.3347 - loss: 1.9763
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Epoch 47/124

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[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3484 - loss: 1.9540 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3475 - loss: 1.9550
[1m 75/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3469 - loss: 1.9544
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3465 - loss: 1.9540
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3453 - loss: 1.9548
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3446 - loss: 1.9553 - val_accuracy: 0.3943 - val_loss: 2.0088
Epoch 48/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3630 - loss: 1.8811 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3596 - loss: 1.8966
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3566 - loss: 1.9061
[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3549 - loss: 1.9120
[1m127/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3533 - loss: 1.9175
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3521 - loss: 1.9210 - val_accuracy: 0.3917 - val_loss: 2.0153
Epoch 49/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3561 - loss: 1.9149 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3575 - loss: 1.9163
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3582 - loss: 1.9205
[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3571 - loss: 1.9248
[1m126/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3561 - loss: 1.9276
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3554 - loss: 1.9289 - val_accuracy: 0.3931 - val_loss: 2.0215
Epoch 50/124

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[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3454 - loss: 1.9197 
[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3417 - loss: 1.9239
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[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3413 - loss: 1.9276
[1m123/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3414 - loss: 1.9288
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3416 - loss: 1.9294 - val_accuracy: 0.3877 - val_loss: 2.0229
Epoch 51/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3413 - loss: 1.9376 
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[1m 79/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3429 - loss: 1.9335
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[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step
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Saved model to disk.

Accuracy capturado en la ejecución 19: 35.26 [%]
F1-score capturado en la ejecución 19: 34.29 [%]

=== EJECUCIÓN 20 ===

--- TRAIN (ejecución 20) ---

--- TEST (ejecución 20) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 409,231 (1.56 MB)
 Trainable params: 409,231 (1.56 MB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 6, 250)
(18469, 6, 250)

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[1m 60/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 855us/step
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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 2ms/step

[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m60/86[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 853us/step
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step  
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 9ms/step

[1m  1/158[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step
[1m 61/158[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 840us/step
[1m119/158[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 855us/step
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 39.55 [%]
Global F1 score (validation) = 38.02 [%]
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[[0.0013711  0.00098711 0.00118912 ... 0.03776989 0.00233915 0.00041486]
 [0.00129599 0.00117021 0.00124427 ... 0.07700961 0.00230183 0.0003544 ]
 [0.00248574 0.00144707 0.00228659 ... 0.0527891  0.0040864  0.00080123]
 ...
 [0.20022655 0.04642113 0.18910009 ... 0.00095311 0.21558337 0.07625779]
 [0.21019284 0.07411653 0.15998282 ... 0.00238774 0.17369317 0.06615929]
 [0.2046484  0.05539088 0.19046944 ... 0.00088281 0.22755264 0.06893545]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 41.78 [%]
Global accuracy score (test) = 35.22 [%]
Global F1 score (train) = 41.69 [%]
Global F1 score (test) = 34.82 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.31      0.31      0.31       184
 CAMINAR CON MÓVIL O LIBRO       0.40      0.36      0.38       184
       CAMINAR USUAL SPEED       0.06      0.02      0.03       184
            CAMINAR ZIGZAG       0.20      0.31      0.24       184
          DE PIE BARRIENDO       0.27      0.21      0.23       184
   DE PIE DOBLANDO TOALLAS       0.35      0.37      0.36       184
    DE PIE MOVIENDO LIBROS       0.35      0.23      0.28       184
          DE PIE USANDO PC       0.26      0.27      0.26       184
        FASE REPOSO CON K5       0.49      0.74      0.59       184
INCREMENTAL CICLOERGOMETRO       0.57      0.60      0.58       184
           SENTADO LEYENDO       0.47      0.38      0.42       184
         SENTADO USANDO PC       0.17      0.16      0.17       184
      SENTADO VIENDO LA TV       0.49      0.48      0.49       184
   SUBIR Y BAJAR ESCALERAS       0.20      0.34      0.25       184
                    TROTAR       0.79      0.53      0.63       161

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

2025-10-28 13:01:55.344891: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-28 13:01:55.356340: 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 00:00:1761652915.369514 1873199 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761652915.373770 1873199 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761652915.383650 1873199 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761652915.383673 1873199 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761652915.383676 1873199 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761652915.383678 1873199 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-28 13:01:55.386934: 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 00:00:1761652917.777432 1873199 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13749 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 409,231 (1.56 MB)
 Trainable params: 409,231 (1.56 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/124
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761652920.315230 1873331 service.cc:152] XLA service 0x7d6124002240 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761652920.315317 1873331 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-28 13:02:00.374480: 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 00:00:1761652920.677910 1873331 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761652924.266502 1873331 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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
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Epoch 2/124

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Epoch 3/124

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Epoch 4/124

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Epoch 5/124

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[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.1458 - loss: 2.7112 - val_accuracy: 0.2478 - val_loss: 2.3474
Epoch 6/124

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Epoch 7/124

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Epoch 8/124

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Epoch 9/124

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[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1750 - loss: 2.5315
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Epoch 10/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1953 - loss: 2.4988 
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[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1865 - loss: 2.5090
[1m127/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1863 - loss: 2.5066
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.1864 - loss: 2.5048 - val_accuracy: 0.3049 - val_loss: 2.2566
Epoch 11/124

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[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1757 - loss: 2.4929 
[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1813 - loss: 2.4843
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[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1861 - loss: 2.4776
[1m120/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1873 - loss: 2.4763
[1m144/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.1885 - loss: 2.4743
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.1886 - loss: 2.4741 - val_accuracy: 0.3099 - val_loss: 2.2469
Epoch 12/124

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[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2121 - loss: 2.4300 
[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2062 - loss: 2.4376
[1m 75/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2037 - loss: 2.4369
[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2024 - loss: 2.4362
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2021 - loss: 2.4354
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2020 - loss: 2.4343 - val_accuracy: 0.3202 - val_loss: 2.2030
Epoch 13/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1851 - loss: 2.4538 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1913 - loss: 2.4436
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1959 - loss: 2.4341
[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1997 - loss: 2.4275
[1m127/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2021 - loss: 2.4232
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2033 - loss: 2.4209 - val_accuracy: 0.3196 - val_loss: 2.1931
Epoch 14/124

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Epoch 15/124

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[1m 97/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2283 - loss: 2.3596
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Epoch 16/124

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Epoch 17/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2387 - loss: 2.3141 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2391 - loss: 2.3188
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[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2389 - loss: 2.3160
[1m126/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2385 - loss: 2.3151
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Epoch 18/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.3281 - loss: 2.1887
[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2715 - loss: 2.2720 
[1m 48/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2629 - loss: 2.2844
[1m 73/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2584 - loss: 2.2906
[1m 96/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2559 - loss: 2.2933
[1m121/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2545 - loss: 2.2934
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2533 - loss: 2.2929 - val_accuracy: 0.3431 - val_loss: 2.1584
Epoch 19/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.2109 - loss: 2.3032
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2440 - loss: 2.2940 
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[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2466 - loss: 2.2833
[1m127/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2466 - loss: 2.2821
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2469 - loss: 2.2813 - val_accuracy: 0.3411 - val_loss: 2.1435
Epoch 20/124

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[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2360 - loss: 2.2799 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2413 - loss: 2.2743
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[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2449 - loss: 2.2683
[1m127/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2453 - loss: 2.2663
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Epoch 21/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.2734 - loss: 2.2428
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2370 - loss: 2.2575 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2435 - loss: 2.2494
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[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2505 - loss: 2.2426
[1m129/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2525 - loss: 2.2412
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2533 - loss: 2.2407 - val_accuracy: 0.3546 - val_loss: 2.1485
Epoch 22/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2656 - loss: 2.1811
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2605 - loss: 2.2179 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2593 - loss: 2.2223
[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2592 - loss: 2.2257
[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2602 - loss: 2.2257
[1m128/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2613 - loss: 2.2248
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2617 - loss: 2.2243 - val_accuracy: 0.3540 - val_loss: 2.1249
Epoch 23/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2656 - loss: 2.1956
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2795 - loss: 2.2127 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2753 - loss: 2.2128
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2722 - loss: 2.2120
[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2703 - loss: 2.2108
[1m128/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2690 - loss: 2.2112
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2685 - loss: 2.2115 - val_accuracy: 0.3480 - val_loss: 2.1322
Epoch 24/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2734 - loss: 2.2592
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2666 - loss: 2.2075 
[1m 53/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2679 - loss: 2.2012
[1m 79/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2691 - loss: 2.1967
[1m105/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2695 - loss: 2.1953
[1m130/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2695 - loss: 2.1955
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2696 - loss: 2.1958 - val_accuracy: 0.3540 - val_loss: 2.1270
Epoch 25/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 33ms/step - accuracy: 0.2969 - loss: 2.0912
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2695 - loss: 2.1715 
[1m 53/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2681 - loss: 2.1788
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2694 - loss: 2.1795
[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2702 - loss: 2.1807
[1m126/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2707 - loss: 2.1818
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2710 - loss: 2.1823 - val_accuracy: 0.3502 - val_loss: 2.1052
Epoch 26/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2500 - loss: 2.2485
[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2625 - loss: 2.1953 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2679 - loss: 2.1881
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2697 - loss: 2.1870
[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2711 - loss: 2.1847
[1m130/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2721 - loss: 2.1830
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2727 - loss: 2.1820 - val_accuracy: 0.3534 - val_loss: 2.1129
Epoch 27/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2969 - loss: 2.1658
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2627 - loss: 2.2056 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2693 - loss: 2.1861
[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2718 - loss: 2.1796
[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2730 - loss: 2.1764
[1m129/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2737 - loss: 2.1752
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2742 - loss: 2.1743 - val_accuracy: 0.3572 - val_loss: 2.1117
Epoch 28/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2799 - loss: 2.1421 
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[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2809 - loss: 2.1481
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Epoch 29/124

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Epoch 30/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2915 - loss: 2.1309 
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[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2908 - loss: 2.1247
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Epoch 31/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2848 - loss: 2.1263 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2884 - loss: 2.1265
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2893 - loss: 2.1260
[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2900 - loss: 2.1250
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2911 - loss: 2.1235
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Epoch 32/124

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[1m 24/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3072 - loss: 2.1181 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3004 - loss: 2.1163
[1m 75/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2981 - loss: 2.1159
[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2967 - loss: 2.1165
[1m127/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2963 - loss: 2.1161
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2960 - loss: 2.1157 - val_accuracy: 0.3621 - val_loss: 2.0736
Epoch 33/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.3281 - loss: 2.1269
[1m 28/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3005 - loss: 2.1040 
[1m 53/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3006 - loss: 2.0966
[1m 79/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2997 - loss: 2.0965
[1m104/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2992 - loss: 2.0960
[1m129/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2991 - loss: 2.0947
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2990 - loss: 2.0939 - val_accuracy: 0.3685 - val_loss: 2.0736
Epoch 34/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.3594 - loss: 2.0206
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3071 - loss: 2.1048 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3090 - loss: 2.0847
[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3080 - loss: 2.0786
[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3069 - loss: 2.0768
[1m127/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3062 - loss: 2.0756
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3053 - loss: 2.0758 - val_accuracy: 0.3649 - val_loss: 2.0610
Epoch 35/124

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Epoch 36/124

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Epoch 37/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2811 - loss: 2.1152 
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[1m 79/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2959 - loss: 2.0847
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Epoch 38/124

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[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3054 - loss: 2.0402 
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[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3132 - loss: 2.0302
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Epoch 39/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3169 - loss: 2.0293 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3123 - loss: 2.0376
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3113 - loss: 2.0393
[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3113 - loss: 2.0390
[1m129/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3116 - loss: 2.0377
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3120 - loss: 2.0370 - val_accuracy: 0.3786 - val_loss: 2.0383
Epoch 40/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3241 - loss: 2.0142 
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[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3210 - loss: 2.0167
[1m104/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3199 - loss: 2.0180
[1m128/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3191 - loss: 2.0191
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3186 - loss: 2.0196 - val_accuracy: 0.3806 - val_loss: 2.0200
Epoch 41/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3312 - loss: 1.9988 
[1m 53/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3265 - loss: 2.0055
[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3236 - loss: 2.0084
[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3224 - loss: 2.0087
[1m129/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3220 - loss: 2.0093
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3217 - loss: 2.0101 - val_accuracy: 0.3724 - val_loss: 2.0221
Epoch 42/124

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[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3408 - loss: 1.9814 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3336 - loss: 1.9923
[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3308 - loss: 1.9975
[1m105/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3297 - loss: 1.9987
[1m131/145[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.3290 - loss: 1.9991
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3285 - loss: 1.9993 - val_accuracy: 0.3893 - val_loss: 2.0117
Epoch 43/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.3125 - loss: 1.9126
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3259 - loss: 1.9636 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3250 - loss: 1.9767
[1m 79/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3243 - loss: 1.9838
[1m104/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3244 - loss: 1.9875
[1m126/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3244 - loss: 1.9889
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3245 - loss: 1.9897 - val_accuracy: 0.3736 - val_loss: 2.0507
Epoch 44/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.3672 - loss: 1.9184
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3478 - loss: 1.9399 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3381 - loss: 1.9629
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3348 - loss: 1.9732
[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3334 - loss: 1.9781
[1m129/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3326 - loss: 1.9809
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3324 - loss: 1.9814 - val_accuracy: 0.3784 - val_loss: 2.0187
Epoch 45/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.3438 - loss: 1.9843
[1m 24/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3318 - loss: 1.9794 
[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3366 - loss: 1.9721
[1m 74/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3372 - loss: 1.9712
[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3364 - loss: 1.9721
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3356 - loss: 1.9730
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3353 - loss: 1.9726 - val_accuracy: 0.3802 - val_loss: 2.0322
Epoch 46/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.3672 - loss: 2.0899
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3454 - loss: 1.9708 
[1m 53/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3390 - loss: 1.9710
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3373 - loss: 1.9679
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3366 - loss: 1.9664
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3366 - loss: 1.9654
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3364 - loss: 1.9651 - val_accuracy: 0.3905 - val_loss: 2.0232
Epoch 47/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.2812 - loss: 1.9967
[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3374 - loss: 1.9491 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3361 - loss: 1.9559
[1m 72/145[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3360 - loss: 1.9578
[1m 97/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3368 - loss: 1.9576
[1m120/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3371 - loss: 1.9581
[1m142/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.3371 - loss: 1.9589
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3372 - loss: 1.9590 - val_accuracy: 0.3879 - val_loss: 2.0296

[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:12[0m 849ms/step
[1m61/86[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 837us/step  
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step 
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 11ms/step
Saved model to disk.

Accuracy capturado en la ejecución 20: 35.22 [%]
F1-score capturado en la ejecución 20: 34.82 [%]

=== EJECUCIÓN 21 ===

--- TRAIN (ejecución 21) ---

--- TEST (ejecución 21) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 409,231 (1.56 MB)
 Trainable params: 409,231 (1.56 MB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 6, 250)
(18469, 6, 250)

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[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m47/86[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step 
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[1m  1/158[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 17ms/step
[1m 55/158[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 932us/step
[1m122/158[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 832us/step
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 38.79 [%]
Global F1 score (validation) = 36.5 [%]
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[[0.00164777 0.00050909 0.00133812 ... 0.02864439 0.00226279 0.00070593]
 [0.00176694 0.00074367 0.00160545 ... 0.06161967 0.00263926 0.00074276]
 [0.00397141 0.00086555 0.00328933 ... 0.05844188 0.00456503 0.00170606]
 ...
 [0.19482502 0.04901806 0.20234315 ... 0.00100531 0.23547141 0.07168967]
 [0.1496943  0.14273414 0.08737402 ... 0.01159283 0.07221784 0.03708836]
 [0.19013077 0.04699608 0.20036909 ... 0.00099264 0.23914751 0.07609115]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 40.19 [%]
Global accuracy score (test) = 31.6 [%]
Global F1 score (train) = 39.23 [%]
Global F1 score (test) = 30.85 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.30      0.29      0.29       184
 CAMINAR CON MÓVIL O LIBRO       0.39      0.36      0.38       184
       CAMINAR USUAL SPEED       0.10      0.04      0.06       184
            CAMINAR ZIGZAG       0.11      0.14      0.12       184
          DE PIE BARRIENDO       0.22      0.22      0.22       184
   DE PIE DOBLANDO TOALLAS       0.37      0.35      0.36       184
    DE PIE MOVIENDO LIBROS       0.37      0.22      0.28       184
          DE PIE USANDO PC       0.19      0.18      0.19       184
        FASE REPOSO CON K5       0.43      0.74      0.54       184
INCREMENTAL CICLOERGOMETRO       0.45      0.66      0.53       184
           SENTADO LEYENDO       0.44      0.28      0.34       184
         SENTADO USANDO PC       0.11      0.08      0.09       184
      SENTADO VIENDO LA TV       0.45      0.33      0.38       184
   SUBIR Y BAJAR ESCALERAS       0.16      0.30      0.21       184
                    TROTAR       0.69      0.57      0.63       161

                  accuracy                           0.32      2737
                 macro avg       0.32      0.32      0.31      2737
              weighted avg       0.32      0.32      0.31      2737

2025-10-28 13:02:43.542741: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-28 13:02:43.554401: 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 00:00:1761652963.567904 1878647 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761652963.572019 1878647 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761652963.582177 1878647 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761652963.582201 1878647 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761652963.582204 1878647 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761652963.582206 1878647 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-28 13:02:43.585362: 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 00:00:1761652965.957977 1878647 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13749 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 409,231 (1.56 MB)
 Trainable params: 409,231 (1.56 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/124
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761652968.520924 1878754 service.cc:152] XLA service 0x706030006140 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761652968.520999 1878754 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-28 13:02:48.578218: 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 00:00:1761652968.868426 1878754 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761652972.468889 1878754 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13:47[0m 6s/step - accuracy: 0.0312 - loss: 3.3401
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[1m 44/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.0690 - loss: 3.2137
[1m 68/145[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.0723 - loss: 3.1989
[1m 95/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.0747 - loss: 3.1839
[1m117/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.0762 - loss: 3.1721
[1m143/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.0777 - loss: 3.1594
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 26ms/step - accuracy: 0.0778 - loss: 3.15852025-10-28 13:02:58.121410: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_316', 12 bytes spill stores, 12 bytes spill loads


[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m12s[0m 43ms/step - accuracy: 0.0778 - loss: 3.1581 - val_accuracy: 0.1876 - val_loss: 2.5185
Epoch 2/124

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Epoch 3/124

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Epoch 4/124

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Epoch 5/124

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[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1356 - loss: 2.7404
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Epoch 6/124

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[1m 48/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1506 - loss: 2.6870
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[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1518 - loss: 2.6804
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1521 - loss: 2.6780
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.1522 - loss: 2.6766 - val_accuracy: 0.2243 - val_loss: 2.3217
Epoch 7/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1513 - loss: 2.6115 
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[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1561 - loss: 2.6160
[1m126/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1566 - loss: 2.6166
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.1569 - loss: 2.6164 - val_accuracy: 0.2365 - val_loss: 2.3116
Epoch 8/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1546 - loss: 2.6313 
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[1m104/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1601 - loss: 2.6126
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Epoch 9/124

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[1m144/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.1737 - loss: 2.5600
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Epoch 10/124

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Epoch 11/124

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[1m 96/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1858 - loss: 2.4828
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Epoch 12/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1771 - loss: 2.4953 
[1m 53/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1841 - loss: 2.4829
[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1880 - loss: 2.4771
[1m104/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1899 - loss: 2.4727
[1m129/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1908 - loss: 2.4703
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.1914 - loss: 2.4689 - val_accuracy: 0.3163 - val_loss: 2.2131
Epoch 13/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1989 - loss: 2.4336 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1966 - loss: 2.4384
[1m 75/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1972 - loss: 2.4366
[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1977 - loss: 2.4348
[1m126/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1978 - loss: 2.4330
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.1979 - loss: 2.4315 - val_accuracy: 0.3240 - val_loss: 2.1988
Epoch 14/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.1484 - loss: 2.4330
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1908 - loss: 2.4148 
[1m 53/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1954 - loss: 2.4115
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1978 - loss: 2.4105
[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1999 - loss: 2.4085
[1m127/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2014 - loss: 2.4067
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2022 - loss: 2.4056 - val_accuracy: 0.3315 - val_loss: 2.2001
Epoch 15/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.2266 - loss: 2.3503
[1m 24/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2114 - loss: 2.3919 
[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2147 - loss: 2.3869
[1m 73/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2169 - loss: 2.3835
[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2177 - loss: 2.3821
[1m123/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2182 - loss: 2.3811
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2183 - loss: 2.3801 - val_accuracy: 0.3389 - val_loss: 2.1863
Epoch 16/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2174 - loss: 2.3405 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2172 - loss: 2.3507
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2186 - loss: 2.3527
[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2197 - loss: 2.3538
[1m126/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2204 - loss: 2.3546
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Epoch 17/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2200 - loss: 2.3492 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2220 - loss: 2.3457
[1m 74/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2235 - loss: 2.3425
[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2245 - loss: 2.3399
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2249 - loss: 2.3385
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2254 - loss: 2.3372 - val_accuracy: 0.3425 - val_loss: 2.1641
Epoch 18/124

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[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2370 - loss: 2.2944 
[1m 48/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2397 - loss: 2.3013
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[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2377 - loss: 2.3071
[1m126/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2374 - loss: 2.3073
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2372 - loss: 2.3075 - val_accuracy: 0.3458 - val_loss: 2.1537
Epoch 19/124

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[1m 24/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2549 - loss: 2.2692 
[1m 48/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2509 - loss: 2.2841
[1m 74/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2499 - loss: 2.2870
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2493 - loss: 2.2865
[1m126/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2486 - loss: 2.2861
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2480 - loss: 2.2868 - val_accuracy: 0.3540 - val_loss: 2.1378
Epoch 20/124

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[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2504 - loss: 2.2731 
[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2478 - loss: 2.2773
[1m 73/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2468 - loss: 2.2797
[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2463 - loss: 2.2793
[1m123/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2459 - loss: 2.2788
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2458 - loss: 2.2783 - val_accuracy: 0.3508 - val_loss: 2.1284
Epoch 21/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2812 - loss: 2.1424
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2499 - loss: 2.2527 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2466 - loss: 2.2597
[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2464 - loss: 2.2605
[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2466 - loss: 2.2608
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2469 - loss: 2.2606
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2475 - loss: 2.2595 - val_accuracy: 0.3480 - val_loss: 2.1506
Epoch 22/124

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[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2718 - loss: 2.1925 
[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2705 - loss: 2.1959
[1m 72/145[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2692 - loss: 2.1990
[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2676 - loss: 2.2043
[1m122/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2663 - loss: 2.2095
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2653 - loss: 2.2135 - val_accuracy: 0.3476 - val_loss: 2.1149
Epoch 23/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2565 - loss: 2.2380 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2585 - loss: 2.2322
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2603 - loss: 2.2276
[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2616 - loss: 2.2252
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2628 - loss: 2.2238
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2636 - loss: 2.2230 - val_accuracy: 0.3609 - val_loss: 2.1122
Epoch 24/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.2891 - loss: 2.2576
[1m 24/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2739 - loss: 2.2033 
[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2709 - loss: 2.2047
[1m 74/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2696 - loss: 2.2057
[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2691 - loss: 2.2064
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2683 - loss: 2.2076
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2679 - loss: 2.2080 - val_accuracy: 0.3633 - val_loss: 2.1040
Epoch 25/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2500 - loss: 2.2226
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2601 - loss: 2.2057 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2641 - loss: 2.2089
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2635 - loss: 2.2135
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2634 - loss: 2.2137
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2636 - loss: 2.2123
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2640 - loss: 2.2104 - val_accuracy: 0.3583 - val_loss: 2.1085
Epoch 26/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.1797 - loss: 2.2905
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2599 - loss: 2.2004 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2664 - loss: 2.1989
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2693 - loss: 2.1956
[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2714 - loss: 2.1916
[1m127/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2723 - loss: 2.1892
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2728 - loss: 2.1882 - val_accuracy: 0.3603 - val_loss: 2.0962
Epoch 27/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.2656 - loss: 2.1409
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2791 - loss: 2.1466 
[1m 54/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2803 - loss: 2.1561
[1m 81/145[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2809 - loss: 2.1594
[1m107/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2804 - loss: 2.1618
[1m133/145[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.2801 - loss: 2.1630
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2799 - loss: 2.1636 - val_accuracy: 0.3689 - val_loss: 2.0974
Epoch 28/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.3203 - loss: 2.1325
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2822 - loss: 2.2064 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2809 - loss: 2.1977
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2817 - loss: 2.1910
[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2817 - loss: 2.1872
[1m123/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2818 - loss: 2.1840
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.2821 - loss: 2.1807
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2821 - loss: 2.1805 - val_accuracy: 0.3649 - val_loss: 2.0947
Epoch 29/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2734 - loss: 2.1097
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2961 - loss: 2.1220 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2945 - loss: 2.1320
[1m 74/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2947 - loss: 2.1317
[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2932 - loss: 2.1332
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2921 - loss: 2.1343
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2913 - loss: 2.1352 - val_accuracy: 0.3661 - val_loss: 2.0827
Epoch 30/124

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[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2659 - loss: 2.1515
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Epoch 31/124

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[1m 28/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2965 - loss: 2.0924 
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[1m104/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2922 - loss: 2.1090
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Epoch 32/124

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[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2872 - loss: 2.1457 
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[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2898 - loss: 2.1322
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2900 - loss: 2.1301
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Epoch 33/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2935 - loss: 2.0746 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2885 - loss: 2.0852
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[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2904 - loss: 2.0880
[1m122/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2916 - loss: 2.0876
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2923 - loss: 2.0885 - val_accuracy: 0.3701 - val_loss: 2.0425
Epoch 34/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3074 - loss: 2.0848 
[1m 53/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3095 - loss: 2.0760
[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3099 - loss: 2.0746
[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3095 - loss: 2.0754
[1m126/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3088 - loss: 2.0769
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3082 - loss: 2.0780 - val_accuracy: 0.3818 - val_loss: 2.0326
Epoch 35/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3003 - loss: 2.0964 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3038 - loss: 2.0892
[1m 75/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3049 - loss: 2.0854
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3049 - loss: 2.0835
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3051 - loss: 2.0811
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3052 - loss: 2.0801 - val_accuracy: 0.3736 - val_loss: 2.0161
Epoch 36/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2959 - loss: 2.0925 
[1m 53/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2957 - loss: 2.0922
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2967 - loss: 2.0916
[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2985 - loss: 2.0884
[1m127/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2993 - loss: 2.0866
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2999 - loss: 2.0853 - val_accuracy: 0.3810 - val_loss: 2.0180
Epoch 37/124

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Epoch 38/124

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Epoch 39/124

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[1m123/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3162 - loss: 2.0367
[1m144/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.3159 - loss: 2.0363
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Epoch 40/124

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[1m 28/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3036 - loss: 2.0630 
[1m 54/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3091 - loss: 2.0511
[1m 80/145[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3120 - loss: 2.0445
[1m105/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3132 - loss: 2.0408
[1m129/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3139 - loss: 2.0385
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Epoch 41/124

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[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3267 - loss: 2.0115 
[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3241 - loss: 2.0095
[1m 73/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3234 - loss: 2.0100
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3237 - loss: 2.0090
[1m126/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3241 - loss: 2.0088
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3241 - loss: 2.0092 - val_accuracy: 0.3778 - val_loss: 2.0114
Epoch 42/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3195 - loss: 2.0171 
[1m 53/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3202 - loss: 2.0152
[1m 80/145[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3221 - loss: 2.0108
[1m106/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3223 - loss: 2.0104
[1m132/145[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.3219 - loss: 2.0106
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3218 - loss: 2.0105 - val_accuracy: 0.3897 - val_loss: 1.9913
Epoch 43/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3136 - loss: 1.9941 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3243 - loss: 1.9839
[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3254 - loss: 1.9835
[1m105/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3256 - loss: 1.9836
[1m130/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3258 - loss: 1.9836
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3258 - loss: 1.9840 - val_accuracy: 0.3893 - val_loss: 1.9951
Epoch 44/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3262 - loss: 2.0176 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3300 - loss: 2.0045
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3308 - loss: 1.9993
[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3313 - loss: 1.9948
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Epoch 45/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3478 - loss: 1.9476 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3427 - loss: 1.9613
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3397 - loss: 1.9655
[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3380 - loss: 1.9678
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Epoch 46/124

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[1m 28/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3230 - loss: 2.0090 
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[1m 79/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3296 - loss: 1.9926
[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3306 - loss: 1.9892
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[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3320 - loss: 1.9846 - val_accuracy: 0.4010 - val_loss: 1.9873
Epoch 47/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3448 - loss: 1.9349 
[1m 54/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3423 - loss: 1.9459
[1m 80/145[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3412 - loss: 1.9513
[1m104/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3407 - loss: 1.9533
[1m128/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3403 - loss: 1.9538
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3401 - loss: 1.9540 - val_accuracy: 0.4100 - val_loss: 1.9454
Epoch 48/124

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[1m 28/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3379 - loss: 1.9742 
[1m 53/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3357 - loss: 1.9711
[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3378 - loss: 1.9634
[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3387 - loss: 1.9591
[1m127/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3393 - loss: 1.9570
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3395 - loss: 1.9563 - val_accuracy: 0.4078 - val_loss: 1.9718
Epoch 49/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3399 - loss: 1.9293 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3404 - loss: 1.9335
[1m 75/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3400 - loss: 1.9376
[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3405 - loss: 1.9388
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3410 - loss: 1.9395
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3411 - loss: 1.9401 - val_accuracy: 0.4068 - val_loss: 1.9654
Epoch 50/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3574 - loss: 1.9055 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3554 - loss: 1.9078
[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3534 - loss: 1.9104
[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3514 - loss: 1.9141
[1m129/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3503 - loss: 1.9166
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3500 - loss: 1.9175 - val_accuracy: 0.3840 - val_loss: 1.9964
Epoch 51/124

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Epoch 52/124

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Saved model to disk.

Accuracy capturado en la ejecución 21: 31.6 [%]
F1-score capturado en la ejecución 21: 30.85 [%]

=== EJECUCIÓN 22 ===

--- TRAIN (ejecución 22) ---

--- TEST (ejecución 22) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 409,231 (1.56 MB)
 Trainable params: 409,231 (1.56 MB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 6, 250)
(18469, 6, 250)

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[1m60/86[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 856us/step
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[1m  1/158[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step
[1m 62/158[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 830us/step
[1m125/158[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 817us/step
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 41.31 [%]
Global F1 score (validation) = 39.61 [%]
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[[0.00171298 0.00097505 0.00146821 ... 0.03822403 0.0022899  0.00083532]
 [0.00184937 0.00145969 0.00205059 ... 0.06803604 0.00289833 0.00110428]
 [0.00145546 0.00060639 0.00160443 ... 0.02279901 0.00234419 0.00091751]
 ...
 [0.18239707 0.04161296 0.19367182 ... 0.00122052 0.2072137  0.09239663]
 [0.18772236 0.07063148 0.1924767  ... 0.00144784 0.19963878 0.07494521]
 [0.17114681 0.04436486 0.19952051 ... 0.0007786  0.23497142 0.09170697]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 42.9 [%]
Global accuracy score (test) = 35.26 [%]
Global F1 score (train) = 42.56 [%]
Global F1 score (test) = 34.39 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.37      0.47      0.42       184
 CAMINAR CON MÓVIL O LIBRO       0.39      0.35      0.37       184
       CAMINAR USUAL SPEED       0.11      0.04      0.06       184
            CAMINAR ZIGZAG       0.20      0.28      0.23       184
          DE PIE BARRIENDO       0.31      0.18      0.23       184
   DE PIE DOBLANDO TOALLAS       0.36      0.36      0.36       184
    DE PIE MOVIENDO LIBROS       0.36      0.29      0.32       184
          DE PIE USANDO PC       0.28      0.27      0.27       184
        FASE REPOSO CON K5       0.44      0.74      0.55       184
INCREMENTAL CICLOERGOMETRO       0.51      0.63      0.56       184
           SENTADO LEYENDO       0.44      0.41      0.43       184
         SENTADO USANDO PC       0.16      0.11      0.13       184
      SENTADO VIENDO LA TV       0.42      0.35      0.38       184
   SUBIR Y BAJAR ESCALERAS       0.17      0.26      0.21       184
                    TROTAR       0.71      0.57      0.63       161

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

2025-10-28 13:03:33.674699: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-28 13:03:33.685964: 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 00:00:1761653013.699064 1884551 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761653013.703274 1884551 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761653013.713137 1884551 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761653013.713163 1884551 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761653013.713174 1884551 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761653013.713176 1884551 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-28 13:03:33.716459: 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 00:00:1761653016.059697 1884551 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13749 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 409,231 (1.56 MB)
 Trainable params: 409,231 (1.56 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/124
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761653018.623246 1884659 service.cc:152] XLA service 0x7c1b68012ee0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761653018.623315 1884659 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-28 13:03:38.679182: 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 00:00:1761653018.980549 1884659 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761653022.532470 1884659 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 2/124

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Epoch 3/124

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Epoch 4/124

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Epoch 5/124

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Epoch 6/124

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Epoch 7/124

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Epoch 8/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1645 - loss: 2.5676 
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[1m104/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1648 - loss: 2.5741
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Epoch 9/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1721 - loss: 2.5440 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1760 - loss: 2.5356
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[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1773 - loss: 2.5305
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1775 - loss: 2.5304
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.1775 - loss: 2.5304 - val_accuracy: 0.2827 - val_loss: 2.2571
Epoch 10/124

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[1m 24/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1846 - loss: 2.4952 
[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1812 - loss: 2.5080
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[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1808 - loss: 2.5146
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1815 - loss: 2.5140
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.1819 - loss: 2.5134 - val_accuracy: 0.2948 - val_loss: 2.2334
Epoch 11/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.1719 - loss: 2.4427
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1788 - loss: 2.4975 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1812 - loss: 2.4933
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1830 - loss: 2.4905
[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1839 - loss: 2.4888
[1m127/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1847 - loss: 2.4871
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.1852 - loss: 2.4858 - val_accuracy: 0.3125 - val_loss: 2.2246
Epoch 12/124

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[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2020 - loss: 2.4127 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2008 - loss: 2.4233
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[1m 97/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2009 - loss: 2.4332
[1m121/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2013 - loss: 2.4349
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.2014 - loss: 2.4357
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2014 - loss: 2.4357 - val_accuracy: 0.3147 - val_loss: 2.2070
Epoch 13/124

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[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1962 - loss: 2.4293 
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[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1979 - loss: 2.4207
[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1996 - loss: 2.4181
[1m129/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2008 - loss: 2.4168
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Epoch 14/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2198 - loss: 2.3968 
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[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2130 - loss: 2.3958
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Epoch 15/124

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[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2117 - loss: 2.3570 
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[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2154 - loss: 2.3572
[1m123/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2163 - loss: 2.3570
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2172 - loss: 2.3570 - val_accuracy: 0.3544 - val_loss: 2.1619
Epoch 16/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2206 - loss: 2.3372 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2197 - loss: 2.3410
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[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2241 - loss: 2.3360
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2248 - loss: 2.3356
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2252 - loss: 2.3357 - val_accuracy: 0.3578 - val_loss: 2.1569
Epoch 17/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2307 - loss: 2.3345 
[1m 48/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2298 - loss: 2.3292
[1m 73/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2312 - loss: 2.3270
[1m 97/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2322 - loss: 2.3252
[1m122/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2328 - loss: 2.3225
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2332 - loss: 2.3205 - val_accuracy: 0.3657 - val_loss: 2.1521
Epoch 18/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.2266 - loss: 2.2100
[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2268 - loss: 2.2868 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2288 - loss: 2.2915
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2301 - loss: 2.2936
[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2308 - loss: 2.2945
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2321 - loss: 2.2935
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2333 - loss: 2.2926 - val_accuracy: 0.3738 - val_loss: 2.1382
Epoch 19/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.1953 - loss: 2.4522
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2572 - loss: 2.2544 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2548 - loss: 2.2573
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[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2507 - loss: 2.2660
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2493 - loss: 2.2690
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2486 - loss: 2.2705 - val_accuracy: 0.3635 - val_loss: 2.1240
Epoch 20/124

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[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2636 - loss: 2.2561 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2605 - loss: 2.2553
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2590 - loss: 2.2536
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2582 - loss: 2.2526
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Epoch 21/124

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[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2594 - loss: 2.2455 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2605 - loss: 2.2373
[1m 74/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2606 - loss: 2.2364
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2593 - loss: 2.2385
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Epoch 22/124

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[1m 24/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2755 - loss: 2.2218 
[1m 48/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2759 - loss: 2.2125
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[1m 95/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2741 - loss: 2.2097
[1m121/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2734 - loss: 2.2089
[1m143/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.2726 - loss: 2.2093
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2725 - loss: 2.2094 - val_accuracy: 0.3685 - val_loss: 2.1043
Epoch 23/124

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[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2769 - loss: 2.2181 
[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2740 - loss: 2.2022
[1m 73/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2722 - loss: 2.1990
[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2712 - loss: 2.1973
[1m122/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2708 - loss: 2.1958
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.2707 - loss: 2.1952
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2707 - loss: 2.1952 - val_accuracy: 0.3695 - val_loss: 2.0908
Epoch 24/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 25ms/step - accuracy: 0.3203 - loss: 2.0552
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2773 - loss: 2.1661 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2748 - loss: 2.1772
[1m 75/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2749 - loss: 2.1786
[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2747 - loss: 2.1796
[1m128/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2743 - loss: 2.1804
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2741 - loss: 2.1806 - val_accuracy: 0.3762 - val_loss: 2.0749
Epoch 25/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.3438 - loss: 2.0722
[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3019 - loss: 2.1395 
[1m 48/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2920 - loss: 2.1554
[1m 73/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2872 - loss: 2.1621
[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2847 - loss: 2.1663
[1m123/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2833 - loss: 2.1683
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.2824 - loss: 2.1695
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2824 - loss: 2.1695 - val_accuracy: 0.3639 - val_loss: 2.0738
Epoch 26/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2969 - loss: 2.2115
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2756 - loss: 2.1948 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2747 - loss: 2.1898
[1m 75/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2766 - loss: 2.1833
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[1m126/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2788 - loss: 2.1735
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Epoch 27/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.2969 - loss: 2.1389
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2772 - loss: 2.1364 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2777 - loss: 2.1461
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[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2793 - loss: 2.1486
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2801 - loss: 2.1488
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Epoch 28/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2840 - loss: 2.1236 
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[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2832 - loss: 2.1190
[1m104/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2835 - loss: 2.1214
[1m127/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2837 - loss: 2.1237
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2839 - loss: 2.1251 - val_accuracy: 0.3641 - val_loss: 2.0725
Epoch 29/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.3828 - loss: 1.8984
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2938 - loss: 2.0958 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2948 - loss: 2.1020
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2942 - loss: 2.1070
[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2935 - loss: 2.1103
[1m127/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2927 - loss: 2.1125
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2924 - loss: 2.1136 - val_accuracy: 0.3693 - val_loss: 2.0572
Epoch 30/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.3438 - loss: 1.9219
[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3096 - loss: 2.0859 
[1m 48/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3033 - loss: 2.0966
[1m 73/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2990 - loss: 2.1034
[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2976 - loss: 2.1042
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2972 - loss: 2.1041
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2967 - loss: 2.1041 - val_accuracy: 0.3772 - val_loss: 2.0592
Epoch 31/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.2812 - loss: 2.1682
[1m 28/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2929 - loss: 2.1014 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2922 - loss: 2.1083
[1m 75/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2929 - loss: 2.1096
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2940 - loss: 2.1070
[1m126/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2943 - loss: 2.1051
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2945 - loss: 2.1046 - val_accuracy: 0.3766 - val_loss: 2.0502
Epoch 32/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.3359 - loss: 2.0831
[1m 23/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2908 - loss: 2.1168 
[1m 46/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2937 - loss: 2.1040
[1m 70/145[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2955 - loss: 2.0968
[1m 96/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2979 - loss: 2.0924
[1m121/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2992 - loss: 2.0906
[1m141/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.2999 - loss: 2.0902
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3001 - loss: 2.0902 - val_accuracy: 0.3750 - val_loss: 2.0284
Epoch 33/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3131 - loss: 2.0743 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3094 - loss: 2.0767
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[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3062 - loss: 2.0773
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Epoch 34/124

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Epoch 35/124

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[1m105/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3107 - loss: 2.0420
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Epoch 36/124

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[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3067 - loss: 2.0814
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[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3106 - loss: 2.0647
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3108 - loss: 2.0616
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Epoch 37/124

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[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3084 - loss: 2.0459
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[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3109 - loss: 2.0445
[1m126/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3117 - loss: 2.0428
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3121 - loss: 2.0415 - val_accuracy: 0.3824 - val_loss: 2.0117
Epoch 38/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.3281 - loss: 2.0982
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3043 - loss: 2.0517 
[1m 53/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3057 - loss: 2.0446
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3071 - loss: 2.0409
[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3081 - loss: 2.0379
[1m128/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3092 - loss: 2.0357
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3099 - loss: 2.0344 - val_accuracy: 0.3869 - val_loss: 2.0188
Epoch 39/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2578 - loss: 2.0087
[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3100 - loss: 2.0205 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3113 - loss: 2.0233
[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3125 - loss: 2.0226
[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3130 - loss: 2.0225
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3139 - loss: 2.0220
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3146 - loss: 2.0214 - val_accuracy: 0.3800 - val_loss: 2.0048
Epoch 40/124

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[1m 53/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3264 - loss: 2.0130
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Epoch 41/124

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Epoch 42/124

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Epoch 43/124

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[1m 48/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3344 - loss: 1.9540
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Epoch 44/124

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[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3419 - loss: 1.9869 
[1m 48/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3397 - loss: 1.9796
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[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3365 - loss: 1.9729
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Epoch 45/124

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[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3431 - loss: 1.9492 
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[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3381 - loss: 1.9502
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Epoch 46/124

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Epoch 47/124

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[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3451 - loss: 1.9581
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3449 - loss: 1.9548
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3446 - loss: 1.9524 - val_accuracy: 0.3985 - val_loss: 1.9965

[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:11[0m 839ms/step
[1m62/86[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 833us/step  
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step 
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 11ms/step
Saved model to disk.

Accuracy capturado en la ejecución 22: 35.26 [%]
F1-score capturado en la ejecución 22: 34.39 [%]

=== EJECUCIÓN 23 ===

--- TRAIN (ejecución 23) ---

--- TEST (ejecución 23) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 409,231 (1.56 MB)
 Trainable params: 409,231 (1.56 MB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 6, 250)
(18469, 6, 250)

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 2ms/step

[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m59/86[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 874us/step
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step 
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 10ms/step

[1m  1/158[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 17ms/step
[1m 60/158[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 858us/step
[1m128/158[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 795us/step
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 39.85 [%]
Global F1 score (validation) = 37.92 [%]
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[[9.7231678e-04 5.7459722e-04 9.3219924e-04 ... 2.7501201e-02
  1.4649261e-03 4.1517676e-04]
 [1.1694418e-03 8.1860594e-04 1.2005660e-03 ... 7.0954829e-02
  1.7955414e-03 4.1866480e-04]
 [4.0390072e-04 1.5685848e-04 4.4250634e-04 ... 1.6525835e-02
  6.1890972e-04 2.0183329e-04]
 ...
 [1.8479435e-01 3.9273925e-02 2.1622159e-01 ... 6.2705146e-04
  2.2802714e-01 7.7685870e-02]
 [2.0030548e-01 5.5892438e-02 2.1327679e-01 ... 8.5206731e-04
  2.1447396e-01 6.9884747e-02]
 [1.9224304e-01 3.7338085e-02 1.9908671e-01 ... 7.6084351e-04
  2.4375546e-01 7.0507906e-02]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 40.66 [%]
Global accuracy score (test) = 32.52 [%]
Global F1 score (train) = 39.94 [%]
Global F1 score (test) = 31.88 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.26      0.22      0.24       184
 CAMINAR CON MÓVIL O LIBRO       0.48      0.34      0.39       184
       CAMINAR USUAL SPEED       0.11      0.05      0.07       184
            CAMINAR ZIGZAG       0.19      0.20      0.19       184
          DE PIE BARRIENDO       0.29      0.25      0.27       184
   DE PIE DOBLANDO TOALLAS       0.35      0.43      0.39       184
    DE PIE MOVIENDO LIBROS       0.27      0.21      0.24       184
          DE PIE USANDO PC       0.23      0.25      0.24       184
        FASE REPOSO CON K5       0.41      0.74      0.53       184
INCREMENTAL CICLOERGOMETRO       0.50      0.61      0.55       184
           SENTADO LEYENDO       0.51      0.30      0.38       184
         SENTADO USANDO PC       0.12      0.08      0.09       184
      SENTADO VIENDO LA TV       0.45      0.39      0.42       184
   SUBIR Y BAJAR ESCALERAS       0.14      0.28      0.19       184
                    TROTAR       0.62      0.55      0.58       161

                  accuracy                           0.33      2737
                 macro avg       0.33      0.33      0.32      2737
              weighted avg       0.33      0.33      0.32      2737

2025-10-28 13:04:21.817498: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-28 13:04:21.828944: 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 00:00:1761653061.842375 1889987 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761653061.846755 1889987 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761653061.856752 1889987 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761653061.856776 1889987 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761653061.856779 1889987 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761653061.856782 1889987 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-28 13:04:21.860028: 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 00:00:1761653064.257255 1889987 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13749 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 409,231 (1.56 MB)
 Trainable params: 409,231 (1.56 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/124
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761653066.818629 1890111 service.cc:152] XLA service 0x7ed43c012600 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761653066.818703 1890111 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-28 13:04:26.876431: 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 00:00:1761653067.177121 1890111 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761653070.904382 1890111 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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
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Epoch 2/124

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Epoch 3/124

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Epoch 4/124

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Epoch 5/124

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Epoch 6/124

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Epoch 7/124

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Epoch 8/124

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Epoch 9/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1631 - loss: 2.5676 
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[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1743 - loss: 2.5505
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Epoch 10/124

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[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1807 - loss: 2.5134
[1m 72/145[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1815 - loss: 2.5128
[1m 97/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1818 - loss: 2.5123
[1m123/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1826 - loss: 2.5111
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.1833 - loss: 2.5094 - val_accuracy: 0.2849 - val_loss: 2.2736
Epoch 11/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1843 - loss: 2.4730 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1855 - loss: 2.4791
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[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1892 - loss: 2.4767
[1m119/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1904 - loss: 2.4756
[1m142/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.1915 - loss: 2.4741
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.1917 - loss: 2.4738 - val_accuracy: 0.3061 - val_loss: 2.2372
Epoch 12/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1943 - loss: 2.4364 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1948 - loss: 2.4420
[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1951 - loss: 2.4423
[1m105/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1963 - loss: 2.4419
[1m131/145[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.1970 - loss: 2.4421
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.1973 - loss: 2.4418 - val_accuracy: 0.3192 - val_loss: 2.2260
Epoch 13/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2188 - loss: 2.4459
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2001 - loss: 2.4265 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2016 - loss: 2.4229
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2029 - loss: 2.4209
[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2044 - loss: 2.4186
[1m129/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2057 - loss: 2.4163
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2065 - loss: 2.4150 - val_accuracy: 0.3315 - val_loss: 2.2259
Epoch 14/124

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[1m143/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.2157 - loss: 2.3785
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Epoch 15/124

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Epoch 16/124

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[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2299 - loss: 2.3345
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Epoch 17/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2243 - loss: 2.3309 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2284 - loss: 2.3246
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[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2314 - loss: 2.3214
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2325 - loss: 2.3199
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2333 - loss: 2.3187 - val_accuracy: 0.3544 - val_loss: 2.1739
Epoch 18/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2313 - loss: 2.3026 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2352 - loss: 2.3052
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2371 - loss: 2.3047
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2378 - loss: 2.3041
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2382 - loss: 2.3035
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2383 - loss: 2.3031 - val_accuracy: 0.3566 - val_loss: 2.1463
Epoch 19/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.2422 - loss: 2.3355
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2401 - loss: 2.2994 
[1m 53/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2395 - loss: 2.2932
[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2410 - loss: 2.2878
[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2424 - loss: 2.2845
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2431 - loss: 2.2821
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2436 - loss: 2.2806 - val_accuracy: 0.3703 - val_loss: 2.1409
Epoch 20/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.2656 - loss: 2.2418
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2551 - loss: 2.2482 
[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2530 - loss: 2.2532
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[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2523 - loss: 2.2584
[1m123/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2521 - loss: 2.2597
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2522 - loss: 2.2601 - val_accuracy: 0.3458 - val_loss: 2.1588
Epoch 21/124

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[1m 28/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2481 - loss: 2.2586 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2484 - loss: 2.2546
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2500 - loss: 2.2528
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2499 - loss: 2.2527
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2501 - loss: 2.2516
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2503 - loss: 2.2510 - val_accuracy: 0.3488 - val_loss: 2.1486
Epoch 22/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2539 - loss: 2.2406 
[1m 53/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2569 - loss: 2.2409
[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2577 - loss: 2.2396
[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2578 - loss: 2.2391
[1m128/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2579 - loss: 2.2381
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2578 - loss: 2.2377 - val_accuracy: 0.3536 - val_loss: 2.1318
Epoch 23/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2500 - loss: 2.2547
[1m 28/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2686 - loss: 2.2236 
[1m 54/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2680 - loss: 2.2162
[1m 80/145[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2679 - loss: 2.2141
[1m105/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2680 - loss: 2.2117
[1m131/145[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.2682 - loss: 2.2100
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2681 - loss: 2.2097 - val_accuracy: 0.3647 - val_loss: 2.1334
Epoch 24/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2109 - loss: 2.2394
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2606 - loss: 2.1923 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2621 - loss: 2.1905
[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2637 - loss: 2.1906
[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2648 - loss: 2.1918
[1m129/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2651 - loss: 2.1931
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2652 - loss: 2.1938 - val_accuracy: 0.3580 - val_loss: 2.1125
Epoch 25/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.2812 - loss: 2.1266
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2719 - loss: 2.1959 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2709 - loss: 2.1970
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2706 - loss: 2.1971
[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2705 - loss: 2.1964
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2707 - loss: 2.1951
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2710 - loss: 2.1938 - val_accuracy: 0.3570 - val_loss: 2.1390
Epoch 26/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2578 - loss: 2.2639
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2751 - loss: 2.1656 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2736 - loss: 2.1681
[1m 75/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2722 - loss: 2.1713
[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2727 - loss: 2.1719
[1m127/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2733 - loss: 2.1721
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2737 - loss: 2.1718 - val_accuracy: 0.3546 - val_loss: 2.1259
Epoch 27/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2891 - loss: 2.1185
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2815 - loss: 2.1571 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2799 - loss: 2.1632
[1m 79/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2784 - loss: 2.1660
[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2780 - loss: 2.1664
[1m127/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2778 - loss: 2.1659
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2779 - loss: 2.1654 - val_accuracy: 0.3605 - val_loss: 2.0989
Epoch 28/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2774 - loss: 2.1512 
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[1m 75/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2804 - loss: 2.1532
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[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2811 - loss: 2.1519
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Epoch 29/124

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[1m 24/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2750 - loss: 2.1556 
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Epoch 30/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2872 - loss: 2.1170 
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[1m105/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2870 - loss: 2.1229
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Epoch 31/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2927 - loss: 2.0921 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2948 - loss: 2.0951
[1m 73/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2948 - loss: 2.0997
[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2943 - loss: 2.1032
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2937 - loss: 2.1066
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2933 - loss: 2.1087 - val_accuracy: 0.3597 - val_loss: 2.0842
Epoch 32/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.3203 - loss: 2.1014
[1m 28/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3001 - loss: 2.1046 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2967 - loss: 2.1093
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2969 - loss: 2.1089
[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2973 - loss: 2.1074
[1m127/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2976 - loss: 2.1073
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2979 - loss: 2.1071 - val_accuracy: 0.3722 - val_loss: 2.0677
Epoch 33/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 27ms/step - accuracy: 0.2812 - loss: 2.0561
[1m 28/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2946 - loss: 2.1074 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2961 - loss: 2.1029
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2979 - loss: 2.0983
[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2984 - loss: 2.0966
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2981 - loss: 2.0965
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2978 - loss: 2.0965 - val_accuracy: 0.3726 - val_loss: 2.0710
Epoch 34/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2891 - loss: 2.1063
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3002 - loss: 2.0734 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3013 - loss: 2.0721
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3021 - loss: 2.0724
[1m104/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3021 - loss: 2.0736
[1m130/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3025 - loss: 2.0738
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3024 - loss: 2.0740 - val_accuracy: 0.3718 - val_loss: 2.0566
Epoch 35/124

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Epoch 36/124

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[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3089 - loss: 2.0658
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Epoch 37/124

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[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3010 - loss: 2.0891
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[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3047 - loss: 2.0744
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Epoch 38/124

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[1m 28/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3170 - loss: 2.0409 
[1m 54/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3168 - loss: 2.0457
[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3175 - loss: 2.0455
[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3175 - loss: 2.0457
[1m129/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3175 - loss: 2.0451
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Epoch 39/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3055 - loss: 2.0598 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3082 - loss: 2.0572
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3108 - loss: 2.0516
[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3119 - loss: 2.0488
[1m127/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3128 - loss: 2.0460
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3133 - loss: 2.0441 - val_accuracy: 0.3824 - val_loss: 2.0303
Epoch 40/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.3828 - loss: 1.9596
[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3291 - loss: 2.0024 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3233 - loss: 2.0122
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3222 - loss: 2.0141
[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3211 - loss: 2.0162
[1m129/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3206 - loss: 2.0178
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3204 - loss: 2.0187 - val_accuracy: 0.3709 - val_loss: 2.0458
Epoch 41/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.2656 - loss: 2.0041
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3137 - loss: 2.0178 
[1m 53/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3168 - loss: 2.0144
[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3183 - loss: 2.0130
[1m105/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3188 - loss: 2.0121
[1m131/145[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.3191 - loss: 2.0115
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3190 - loss: 2.0117 - val_accuracy: 0.3683 - val_loss: 2.0173
Epoch 42/124

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Epoch 43/124

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Epoch 44/124

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[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3320 - loss: 1.9926
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Epoch 45/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3495 - loss: 1.9815 
[1m 53/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3423 - loss: 1.9841
[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3390 - loss: 1.9851
[1m106/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3372 - loss: 1.9841
[1m132/145[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.3364 - loss: 1.9832
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Epoch 46/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3248 - loss: 1.9774 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3283 - loss: 1.9756
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[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3316 - loss: 1.9727
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3321 - loss: 1.9728
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3326 - loss: 1.9725 - val_accuracy: 0.3810 - val_loss: 2.0275
Epoch 47/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3323 - loss: 1.9805 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3350 - loss: 1.9719
[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3354 - loss: 1.9704
[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3351 - loss: 1.9696
[1m127/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3351 - loss: 1.9682
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3351 - loss: 1.9673 - val_accuracy: 0.3784 - val_loss: 2.0048
Epoch 48/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3324 - loss: 1.9628 
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[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3315 - loss: 1.9692
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3323 - loss: 1.9673
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Epoch 49/124

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[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3367 - loss: 1.9511
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[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3374 - loss: 1.9519
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Epoch 50/124

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[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3380 - loss: 1.9359
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3400 - loss: 1.9343
[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3407 - loss: 1.9351
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Epoch 51/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3442 - loss: 1.9509 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3450 - loss: 1.9472
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3447 - loss: 1.9460
[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3445 - loss: 1.9441
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Epoch 52/124

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[1m 28/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3583 - loss: 1.9318 
[1m 54/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3532 - loss: 1.9307
[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3520 - loss: 1.9297
[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3506 - loss: 1.9294
[1m126/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3496 - loss: 1.9290
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3493 - loss: 1.9285 - val_accuracy: 0.3893 - val_loss: 2.0212
Epoch 53/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.3125 - loss: 1.9397
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3343 - loss: 1.9262 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3434 - loss: 1.9144
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3471 - loss: 1.9082
[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3478 - loss: 1.9090
[1m127/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3480 - loss: 1.9112
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3483 - loss: 1.9119 - val_accuracy: 0.3903 - val_loss: 2.0273
Epoch 54/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.3125 - loss: 1.9786
[1m 24/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3437 - loss: 1.9308 
[1m 48/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3465 - loss: 1.9201
[1m 73/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3485 - loss: 1.9197
[1m 97/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3501 - loss: 1.9192
[1m122/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3515 - loss: 1.9181
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3522 - loss: 1.9175 - val_accuracy: 0.3927 - val_loss: 2.0077
Epoch 55/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.3438 - loss: 1.8599
[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3707 - loss: 1.8837 
[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3693 - loss: 1.8891
[1m 74/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3673 - loss: 1.8957
[1m 97/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3656 - loss: 1.8994
[1m122/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3643 - loss: 1.9012
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3632 - loss: 1.9023 - val_accuracy: 0.4046 - val_loss: 1.9870
Epoch 56/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.3906 - loss: 1.8840
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3573 - loss: 1.8976 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3560 - loss: 1.8973
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3573 - loss: 1.8940
[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3583 - loss: 1.8912
[1m127/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3586 - loss: 1.8907
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3585 - loss: 1.8911 - val_accuracy: 0.3939 - val_loss: 2.0098
Epoch 57/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.3203 - loss: 2.0279
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3463 - loss: 1.9294 
[1m 53/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3529 - loss: 1.9109
[1m 79/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3542 - loss: 1.9054
[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3551 - loss: 1.9024
[1m128/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3560 - loss: 1.9000
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3565 - loss: 1.8986 - val_accuracy: 0.4016 - val_loss: 2.0029
Epoch 58/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.4219 - loss: 1.8273
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3701 - loss: 1.8872 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3666 - loss: 1.8894
[1m 75/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3650 - loss: 1.8871
[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3640 - loss: 1.8864
[1m126/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3640 - loss: 1.8852
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3641 - loss: 1.8840 - val_accuracy: 0.3985 - val_loss: 2.0101
Epoch 59/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.3594 - loss: 1.8427
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3614 - loss: 1.8573 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3585 - loss: 1.8704
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3579 - loss: 1.8778
[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3588 - loss: 1.8798
[1m128/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3598 - loss: 1.8798
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3606 - loss: 1.8794 - val_accuracy: 0.4092 - val_loss: 1.9925
Epoch 60/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.3984 - loss: 1.8628
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3643 - loss: 1.8729 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3645 - loss: 1.8704
[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3657 - loss: 1.8676
[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3658 - loss: 1.8662
[1m128/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3658 - loss: 1.8655
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3658 - loss: 1.8653 - val_accuracy: 0.4004 - val_loss: 2.0023

[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:13[0m 868ms/step
[1m62/86[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 820us/step  
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step 
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 10ms/step
Saved model to disk.

Accuracy capturado en la ejecución 23: 32.52 [%]
F1-score capturado en la ejecución 23: 31.88 [%]

=== EJECUCIÓN 24 ===

--- TRAIN (ejecución 24) ---

--- TEST (ejecución 24) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 409,231 (1.56 MB)
 Trainable params: 409,231 (1.56 MB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 6, 250)
(18469, 6, 250)

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[1m 62/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 830us/step
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[1m307/578[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 824us/step
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[1m  1/158[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step
[1m 63/158[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 809us/step
[1m130/158[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 781us/step
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 40.04 [%]
Global F1 score (validation) = 38.01 [%]
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[[0.0018928  0.00070039 0.00224179 ... 0.02908852 0.00269254 0.00088615]
 [0.00148399 0.00096845 0.00245517 ... 0.06376208 0.00300232 0.00099438]
 [0.00166537 0.00042792 0.00213829 ... 0.0247352  0.00284587 0.00095911]
 ...
 [0.19571769 0.05558277 0.19642419 ... 0.00054585 0.22179149 0.06693805]
 [0.21626028 0.09302443 0.17389955 ... 0.00128757 0.18900385 0.05774818]
 [0.17848852 0.04381844 0.21546048 ... 0.00041827 0.25450155 0.06620765]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 45.6 [%]
Global accuracy score (test) = 33.07 [%]
Global F1 score (train) = 45.2 [%]
Global F1 score (test) = 31.85 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.35      0.51      0.42       184
 CAMINAR CON MÓVIL O LIBRO       0.32      0.31      0.31       184
       CAMINAR USUAL SPEED       0.14      0.04      0.06       184
            CAMINAR ZIGZAG       0.16      0.14      0.15       184
          DE PIE BARRIENDO       0.26      0.28      0.27       184
   DE PIE DOBLANDO TOALLAS       0.33      0.29      0.31       184
    DE PIE MOVIENDO LIBROS       0.33      0.21      0.26       184
          DE PIE USANDO PC       0.26      0.29      0.27       184
        FASE REPOSO CON K5       0.43      0.74      0.55       184
INCREMENTAL CICLOERGOMETRO       0.53      0.64      0.58       184
           SENTADO LEYENDO       0.36      0.31      0.33       184
         SENTADO USANDO PC       0.11      0.05      0.07       184
      SENTADO VIENDO LA TV       0.40      0.31      0.35       184
   SUBIR Y BAJAR ESCALERAS       0.16      0.32      0.22       184
                    TROTAR       0.73      0.56      0.63       161

                  accuracy                           0.33      2737
                 macro avg       0.33      0.33      0.32      2737
              weighted avg       0.32      0.33      0.32      2737

2025-10-28 13:05:15.385157: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-28 13:05:15.396825: 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 00:00:1761653115.410608 1896626 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761653115.414740 1896626 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761653115.425000 1896626 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761653115.425025 1896626 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761653115.425028 1896626 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761653115.425030 1896626 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-28 13:05:15.428386: 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 00:00:1761653117.820079 1896626 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13749 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 409,231 (1.56 MB)
 Trainable params: 409,231 (1.56 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/124
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761653120.390121 1896738 service.cc:152] XLA service 0x700780012ea0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761653120.390188 1896738 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-28 13:05:20.444747: 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 00:00:1761653120.735518 1896738 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761653124.477129 1896738 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m14:07[0m 6s/step - accuracy: 0.0703 - loss: 3.2743
[1m 19/145[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.0752 - loss: 3.1965  
[1m 42/145[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.0743 - loss: 3.1798
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[1m 93/145[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.0783 - loss: 3.1510
[1m119/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.0796 - loss: 3.1394
[1m144/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.0808 - loss: 3.1296
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 27ms/step - accuracy: 0.0808 - loss: 3.12922025-10-28 13:05:30.142927: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_316', 12 bytes spill stores, 12 bytes spill loads


[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m12s[0m 44ms/step - accuracy: 0.0809 - loss: 3.1289 - val_accuracy: 0.1920 - val_loss: 2.4289
Epoch 2/124

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[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.0921 - loss: 3.0084
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[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.0957 - loss: 2.9832 - val_accuracy: 0.1902 - val_loss: 2.3867
Epoch 3/124

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Epoch 4/124

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Epoch 5/124

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Epoch 6/124

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[1m 97/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1463 - loss: 2.6720
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Epoch 7/124

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[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1604 - loss: 2.6459
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1611 - loss: 2.6418
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.1618 - loss: 2.6381 - val_accuracy: 0.2557 - val_loss: 2.3231
Epoch 8/124

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[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1678 - loss: 2.5915
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[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.1685 - loss: 2.5867 - val_accuracy: 0.2781 - val_loss: 2.2855
Epoch 9/124

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Epoch 10/124

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[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1879 - loss: 2.4948
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Epoch 11/124

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[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1861 - loss: 2.4885
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Epoch 12/124

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[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2024 - loss: 2.4497
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Epoch 13/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2040 - loss: 2.4232 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2099 - loss: 2.4190
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2116 - loss: 2.4161
[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2122 - loss: 2.4151
[1m128/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2120 - loss: 2.4152
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2117 - loss: 2.4149 - val_accuracy: 0.3194 - val_loss: 2.1992
Epoch 14/124

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[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2157 - loss: 2.3953 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2140 - loss: 2.4010
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2138 - loss: 2.4016
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2132 - loss: 2.4012
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2128 - loss: 2.4007
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2127 - loss: 2.3996 - val_accuracy: 0.3347 - val_loss: 2.1901
Epoch 15/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 28ms/step - accuracy: 0.2031 - loss: 2.4692
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2237 - loss: 2.3636 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2225 - loss: 2.3654
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2218 - loss: 2.3660
[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2210 - loss: 2.3670
[1m127/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2207 - loss: 2.3683
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2205 - loss: 2.3687 - val_accuracy: 0.3450 - val_loss: 2.1823
Epoch 16/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.2031 - loss: 2.4401
[1m 23/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2122 - loss: 2.3726 
[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2194 - loss: 2.3617
[1m 75/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2216 - loss: 2.3571
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2224 - loss: 2.3559
[1m127/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2229 - loss: 2.3548
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2230 - loss: 2.3546 - val_accuracy: 0.3450 - val_loss: 2.1659
Epoch 17/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2417 - loss: 2.3336 
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[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2347 - loss: 2.3311
[1m126/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2348 - loss: 2.3281
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2346 - loss: 2.3269 - val_accuracy: 0.3556 - val_loss: 2.1554
Epoch 18/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2177 - loss: 2.3279 
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[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2278 - loss: 2.3255
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Epoch 19/124

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[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2415 - loss: 2.2709
[1m123/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2418 - loss: 2.2739
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Epoch 20/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2504 - loss: 2.2338 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2474 - loss: 2.2469
[1m 74/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2463 - loss: 2.2533
[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2464 - loss: 2.2567
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2468 - loss: 2.2592
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2468 - loss: 2.2606 - val_accuracy: 0.3583 - val_loss: 2.1459
Epoch 21/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2266 - loss: 2.3246
[1m 28/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2394 - loss: 2.2875 
[1m 53/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2415 - loss: 2.2824
[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2449 - loss: 2.2740
[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2480 - loss: 2.2692
[1m127/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2503 - loss: 2.2650
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2515 - loss: 2.2626 - val_accuracy: 0.3540 - val_loss: 2.1222
Epoch 22/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.2344 - loss: 2.2754
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2497 - loss: 2.2518 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2512 - loss: 2.2530
[1m 74/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2534 - loss: 2.2507
[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2558 - loss: 2.2470
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2570 - loss: 2.2444
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2576 - loss: 2.2432 - val_accuracy: 0.3562 - val_loss: 2.1181
Epoch 23/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.1797 - loss: 2.4290
[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2495 - loss: 2.2473 
[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2549 - loss: 2.2393
[1m 74/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2579 - loss: 2.2353
[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2598 - loss: 2.2321
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2613 - loss: 2.2296
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2621 - loss: 2.2284 - val_accuracy: 0.3516 - val_loss: 2.1137
Epoch 24/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2627 - loss: 2.1866 
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[1m 79/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2617 - loss: 2.1990
[1m105/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2620 - loss: 2.2004
[1m130/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2624 - loss: 2.2012
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2627 - loss: 2.2015 - val_accuracy: 0.3566 - val_loss: 2.1238
Epoch 25/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.2578 - loss: 2.1843
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2773 - loss: 2.1708 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2754 - loss: 2.1756
[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2740 - loss: 2.1810
[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2731 - loss: 2.1851
[1m126/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2726 - loss: 2.1876
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2725 - loss: 2.1890 - val_accuracy: 0.3689 - val_loss: 2.1158
Epoch 26/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2500 - loss: 2.2598
[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2660 - loss: 2.1920 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2751 - loss: 2.1855
[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2782 - loss: 2.1814
[1m105/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2788 - loss: 2.1792
[1m128/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2789 - loss: 2.1778
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2790 - loss: 2.1763 - val_accuracy: 0.3615 - val_loss: 2.1074
Epoch 27/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 27ms/step - accuracy: 0.2812 - loss: 2.1360
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2638 - loss: 2.1485 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2695 - loss: 2.1521
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2720 - loss: 2.1525
[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2733 - loss: 2.1550
[1m128/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2742 - loss: 2.1566
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2746 - loss: 2.1577 - val_accuracy: 0.3631 - val_loss: 2.0980
Epoch 28/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.2734 - loss: 2.1940
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2623 - loss: 2.1724 
[1m 53/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2714 - loss: 2.1643
[1m 79/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2752 - loss: 2.1607
[1m104/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2777 - loss: 2.1578
[1m130/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2790 - loss: 2.1572
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2794 - loss: 2.1569 - val_accuracy: 0.3681 - val_loss: 2.0943
Epoch 29/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2656 - loss: 2.1624
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2701 - loss: 2.1557 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2757 - loss: 2.1528
[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2795 - loss: 2.1460
[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2819 - loss: 2.1418
[1m126/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2832 - loss: 2.1397
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2840 - loss: 2.1386 - val_accuracy: 0.3637 - val_loss: 2.0791
Epoch 30/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.2891 - loss: 2.0875
[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2749 - loss: 2.1427 
[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2783 - loss: 2.1340
[1m 74/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2816 - loss: 2.1273
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2830 - loss: 2.1252
[1m122/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2838 - loss: 2.1243
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2848 - loss: 2.1237 - val_accuracy: 0.3570 - val_loss: 2.0945
Epoch 31/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2844 - loss: 2.1220 
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Epoch 32/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2898 - loss: 2.1042 
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Epoch 33/124

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[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2957 - loss: 2.1038
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Epoch 34/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3096 - loss: 2.0883 
[1m 53/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3077 - loss: 2.0787
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3069 - loss: 2.0772
[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3064 - loss: 2.0765
[1m128/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3057 - loss: 2.0774
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3053 - loss: 2.0781 - val_accuracy: 0.3762 - val_loss: 2.0705
Epoch 35/124

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[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2988 - loss: 2.0823 
[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3015 - loss: 2.0824
[1m 75/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3028 - loss: 2.0804
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3033 - loss: 2.0789
[1m126/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3039 - loss: 2.0764
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3042 - loss: 2.0750 - val_accuracy: 0.3766 - val_loss: 2.0675
Epoch 36/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2578 - loss: 2.1217
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2729 - loss: 2.1319 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2840 - loss: 2.1099
[1m 74/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2905 - loss: 2.0962
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2949 - loss: 2.0887
[1m126/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2975 - loss: 2.0836
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2991 - loss: 2.0803 - val_accuracy: 0.3621 - val_loss: 2.0957
Epoch 37/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.3281 - loss: 2.0824
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3125 - loss: 2.0353 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3132 - loss: 2.0373
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3150 - loss: 2.0370
[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3156 - loss: 2.0368
[1m126/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3153 - loss: 2.0377
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3150 - loss: 2.0388 - val_accuracy: 0.3726 - val_loss: 2.0510
Epoch 38/124

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Epoch 39/124

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Epoch 40/124

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[1m104/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3321 - loss: 2.0066
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Epoch 41/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3336 - loss: 1.9805 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3339 - loss: 1.9923
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[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3326 - loss: 1.9966
[1m129/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3313 - loss: 1.9993
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3305 - loss: 2.0010 - val_accuracy: 0.3846 - val_loss: 2.0298
Epoch 42/124

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[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3408 - loss: 1.9870 
[1m 47/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3357 - loss: 1.9888
[1m 73/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3341 - loss: 1.9923
[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3337 - loss: 1.9951
[1m122/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3335 - loss: 1.9969
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3330 - loss: 1.9983 - val_accuracy: 0.3903 - val_loss: 2.0205
Epoch 43/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.3672 - loss: 2.0294
[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3235 - loss: 2.0005 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3219 - loss: 2.0005
[1m 73/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3217 - loss: 2.0009
[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3231 - loss: 1.9989
[1m123/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3244 - loss: 1.9969
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3250 - loss: 1.9958 - val_accuracy: 0.3824 - val_loss: 2.0217
Epoch 44/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.3359 - loss: 1.9600
[1m 24/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3182 - loss: 1.9941 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3244 - loss: 1.9845
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3284 - loss: 1.9807
[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3302 - loss: 1.9783
[1m126/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3309 - loss: 1.9774
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3311 - loss: 1.9772 - val_accuracy: 0.3822 - val_loss: 2.0241
Epoch 45/124

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Epoch 46/124

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Epoch 47/124

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[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3484 - loss: 1.9213
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[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3453 - loss: 1.9302
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3444 - loss: 1.9339
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Epoch 48/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3332 - loss: 1.9636 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3368 - loss: 1.9562
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[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3389 - loss: 1.9537
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3393 - loss: 1.9534
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Epoch 49/124

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[1m 28/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3412 - loss: 1.9519 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3424 - loss: 1.9503
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[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3433 - loss: 1.9435
[1m122/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3441 - loss: 1.9415
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3447 - loss: 1.9402 - val_accuracy: 0.3903 - val_loss: 2.0148
Epoch 50/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3515 - loss: 1.9295 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3530 - loss: 1.9248
[1m 79/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3519 - loss: 1.9250
[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3513 - loss: 1.9246
[1m129/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3512 - loss: 1.9240
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3512 - loss: 1.9237 - val_accuracy: 0.3949 - val_loss: 2.0093
Epoch 51/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3605 - loss: 1.8741 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3616 - loss: 1.8859
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[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3589 - loss: 1.9025
[1m128/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3576 - loss: 1.9068
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Epoch 52/124

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Epoch 53/124

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Saved model to disk.

Accuracy capturado en la ejecución 24: 33.07 [%]
F1-score capturado en la ejecución 24: 31.85 [%]

=== EJECUCIÓN 25 ===

--- TRAIN (ejecución 25) ---

--- TEST (ejecución 25) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 409,231 (1.56 MB)
 Trainable params: 409,231 (1.56 MB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 6, 250)
(18469, 6, 250)

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[1m55/86[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 939us/step
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[1m 57/158[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 897us/step
[1m122/158[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 831us/step
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 38.57 [%]
Global F1 score (validation) = 36.26 [%]
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[[0.00115397 0.00084031 0.00120118 ... 0.02370161 0.00185775 0.00038373]
 [0.00203114 0.00173907 0.00213826 ... 0.05930153 0.00359027 0.00063391]
 [0.00095945 0.0005896  0.00107682 ... 0.02717055 0.0016518  0.0003542 ]
 ...
 [0.20245047 0.05100783 0.18518235 ... 0.00087324 0.2432982  0.06902901]
 [0.22035015 0.10045081 0.13657261 ... 0.00293956 0.1788889  0.06357266]
 [0.22126147 0.06427968 0.18045998 ... 0.00102605 0.24764007 0.05247932]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 43.46 [%]
Global accuracy score (test) = 33.14 [%]
Global F1 score (train) = 42.7 [%]
Global F1 score (test) = 32.66 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.28      0.40      0.33       184
 CAMINAR CON MÓVIL O LIBRO       0.36      0.36      0.36       184
       CAMINAR USUAL SPEED       0.16      0.08      0.10       184
            CAMINAR ZIGZAG       0.06      0.04      0.05       184
          DE PIE BARRIENDO       0.31      0.26      0.28       184
   DE PIE DOBLANDO TOALLAS       0.31      0.22      0.26       184
    DE PIE MOVIENDO LIBROS       0.45      0.27      0.34       184
          DE PIE USANDO PC       0.21      0.25      0.23       184
        FASE REPOSO CON K5       0.42      0.74      0.54       184
INCREMENTAL CICLOERGOMETRO       0.54      0.63      0.58       184
           SENTADO LEYENDO       0.52      0.38      0.44       184
         SENTADO USANDO PC       0.19      0.14      0.16       184
      SENTADO VIENDO LA TV       0.41      0.34      0.38       184
   SUBIR Y BAJAR ESCALERAS       0.15      0.33      0.21       184
                    TROTAR       0.76      0.57      0.65       161

                  accuracy                           0.33      2737
                 macro avg       0.34      0.33      0.33      2737
              weighted avg       0.34      0.33      0.32      2737

2025-10-28 13:06:06.313560: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-28 13:06:06.324892: 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 00:00:1761653166.338029 1902715 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761653166.342304 1902715 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761653166.352238 1902715 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761653166.352260 1902715 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761653166.352263 1902715 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761653166.352265 1902715 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-28 13:06:06.355542: 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 00:00:1761653168.734536 1902715 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13749 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 409,231 (1.56 MB)
 Trainable params: 409,231 (1.56 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/124
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761653171.279919 1902845 service.cc:152] XLA service 0x7a9adc011890 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761653171.279995 1902845 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-28 13:06:11.337479: 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 00:00:1761653171.659965 1902845 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761653175.279292 1902845 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 2/124

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Epoch 3/124

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Epoch 4/124

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Epoch 5/124

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Epoch 6/124

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Epoch 7/124

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Epoch 8/124

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Epoch 9/124

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[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1696 - loss: 2.5431 
[1m 48/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1696 - loss: 2.5477
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[1m 97/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1705 - loss: 2.5476
[1m123/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1717 - loss: 2.5450
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.1726 - loss: 2.5429 - val_accuracy: 0.2734 - val_loss: 2.2859
Epoch 10/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1747 - loss: 2.5421 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1795 - loss: 2.5297
[1m 72/145[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1801 - loss: 2.5235
[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1802 - loss: 2.5197
[1m123/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1803 - loss: 2.5171
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.1804 - loss: 2.5152 - val_accuracy: 0.2775 - val_loss: 2.2445
Epoch 11/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.2422 - loss: 2.4860
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2031 - loss: 2.4609 
[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2028 - loss: 2.4569
[1m 74/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2016 - loss: 2.4587
[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2000 - loss: 2.4615
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1985 - loss: 2.4639
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.1978 - loss: 2.4648 - val_accuracy: 0.2797 - val_loss: 2.2319
Epoch 12/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.1953 - loss: 2.3773
[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2108 - loss: 2.3938 
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[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2002 - loss: 2.4263
[1m122/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1996 - loss: 2.4290
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.1993 - loss: 2.4311 - val_accuracy: 0.3065 - val_loss: 2.2089
Epoch 13/124

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Epoch 14/124

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Epoch 15/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2124 - loss: 2.3725 
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[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2176 - loss: 2.3700
[1m104/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2177 - loss: 2.3695
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Epoch 16/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2202 - loss: 2.3541 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2198 - loss: 2.3545
[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2198 - loss: 2.3543
[1m104/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2198 - loss: 2.3538
[1m128/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2203 - loss: 2.3522
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2207 - loss: 2.3510 - val_accuracy: 0.3381 - val_loss: 2.1738
Epoch 17/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2500 - loss: 2.2826
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2334 - loss: 2.3251 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2316 - loss: 2.3299
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2310 - loss: 2.3314
[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2308 - loss: 2.3304
[1m126/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2310 - loss: 2.3297
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2312 - loss: 2.3288 - val_accuracy: 0.3363 - val_loss: 2.1682
Epoch 18/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.2734 - loss: 2.4311
[1m 23/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2524 - loss: 2.3217 
[1m 48/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2468 - loss: 2.3153
[1m 70/145[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2447 - loss: 2.3108
[1m 93/145[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2431 - loss: 2.3088
[1m120/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2418 - loss: 2.3074
[1m143/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.2413 - loss: 2.3061
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2412 - loss: 2.3059 - val_accuracy: 0.3429 - val_loss: 2.1553
Epoch 19/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2541 - loss: 2.2826 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2494 - loss: 2.2826
[1m 75/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2476 - loss: 2.2807
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2464 - loss: 2.2803
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2458 - loss: 2.2803
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2453 - loss: 2.2806 - val_accuracy: 0.3506 - val_loss: 2.1528
Epoch 20/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2495 - loss: 2.2624 
[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2476 - loss: 2.2652
[1m 74/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2481 - loss: 2.2657
[1m 95/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2485 - loss: 2.2664
[1m118/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2491 - loss: 2.2668
[1m142/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.2497 - loss: 2.2669
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2498 - loss: 2.2669 - val_accuracy: 0.3470 - val_loss: 2.1401
Epoch 21/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2969 - loss: 2.2702
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2554 - loss: 2.2788 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2560 - loss: 2.2685
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2560 - loss: 2.2638
[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2563 - loss: 2.2592
[1m126/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2563 - loss: 2.2567
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2561 - loss: 2.2555 - val_accuracy: 0.3478 - val_loss: 2.1391
Epoch 22/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2581 - loss: 2.2525 
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[1m 75/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2602 - loss: 2.2413
[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2599 - loss: 2.2402
[1m122/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2597 - loss: 2.2393
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2598 - loss: 2.2382 - val_accuracy: 0.3518 - val_loss: 2.1303
Epoch 23/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2422 - loss: 2.2542
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2664 - loss: 2.1994 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2652 - loss: 2.2072
[1m 75/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2657 - loss: 2.2096
[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2655 - loss: 2.2120
[1m122/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2649 - loss: 2.2142
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2644 - loss: 2.2160 - val_accuracy: 0.3520 - val_loss: 2.1304
Epoch 24/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.1875 - loss: 2.3489
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2692 - loss: 2.2022 
[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2711 - loss: 2.2044
[1m 74/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2709 - loss: 2.2057
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2708 - loss: 2.2052
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2703 - loss: 2.2053
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2700 - loss: 2.2053 - val_accuracy: 0.3558 - val_loss: 2.1241
Epoch 25/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2812 - loss: 2.1566
[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2813 - loss: 2.1759 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2830 - loss: 2.1763
[1m 74/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2816 - loss: 2.1802
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2791 - loss: 2.1862
[1m126/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2777 - loss: 2.1890
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2772 - loss: 2.1898 - val_accuracy: 0.3411 - val_loss: 2.1301
Epoch 26/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2422 - loss: 2.2515
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2725 - loss: 2.1924 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2739 - loss: 2.1897
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2747 - loss: 2.1860
[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2759 - loss: 2.1833
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2763 - loss: 2.1819
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2766 - loss: 2.1812 - val_accuracy: 0.3526 - val_loss: 2.1052
Epoch 27/124

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[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2724 - loss: 2.1917 
[1m 47/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2699 - loss: 2.1870
[1m 74/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2704 - loss: 2.1802
[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2711 - loss: 2.1775
[1m122/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2718 - loss: 2.1749
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2724 - loss: 2.1738 - val_accuracy: 0.3506 - val_loss: 2.1225
Epoch 28/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2803 - loss: 2.1542 
[1m 53/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2815 - loss: 2.1524
[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2817 - loss: 2.1517
[1m104/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2818 - loss: 2.1516
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2815 - loss: 2.1521
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2814 - loss: 2.1522 - val_accuracy: 0.3492 - val_loss: 2.1128
Epoch 29/124

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[1m 24/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2849 - loss: 2.1758 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2847 - loss: 2.1655
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[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2844 - loss: 2.1583
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2849 - loss: 2.1548
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2853 - loss: 2.1522 - val_accuracy: 0.3611 - val_loss: 2.0963
Epoch 30/124

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[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2849 - loss: 2.1592 
[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2827 - loss: 2.1584
[1m 74/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2829 - loss: 2.1550
[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2829 - loss: 2.1514
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2828 - loss: 2.1491
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2831 - loss: 2.1472 - val_accuracy: 0.3558 - val_loss: 2.0999
Epoch 31/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.3750 - loss: 2.1100
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3053 - loss: 2.1033 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3004 - loss: 2.1147
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2976 - loss: 2.1191
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2960 - loss: 2.1208
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2950 - loss: 2.1223
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2943 - loss: 2.1233 - val_accuracy: 0.3587 - val_loss: 2.0941
Epoch 32/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.2891 - loss: 2.1281
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2992 - loss: 2.1176 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2970 - loss: 2.1159
[1m 72/145[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2972 - loss: 2.1151
[1m 94/145[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2977 - loss: 2.1135
[1m117/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2981 - loss: 2.1126
[1m141/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.2981 - loss: 2.1121
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2981 - loss: 2.1120 - val_accuracy: 0.3625 - val_loss: 2.0741
Epoch 33/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2975 - loss: 2.1136 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2950 - loss: 2.1143
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2953 - loss: 2.1126
[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2960 - loss: 2.1099
[1m123/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2961 - loss: 2.1080
[1m144/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.2961 - loss: 2.1067
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Epoch 34/124

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Epoch 35/124

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[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2974 - loss: 2.0886
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Epoch 36/124

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[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3213 - loss: 2.0370 
[1m 48/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3191 - loss: 2.0388
[1m 73/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3148 - loss: 2.0450
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3114 - loss: 2.0517
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3097 - loss: 2.0550
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Epoch 37/124

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[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3106 - loss: 2.0598 
[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3132 - loss: 2.0590
[1m 74/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3135 - loss: 2.0617
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3133 - loss: 2.0633
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3130 - loss: 2.0626
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3125 - loss: 2.0628 - val_accuracy: 0.3681 - val_loss: 2.0881
Epoch 38/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3134 - loss: 2.0624 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3149 - loss: 2.0590
[1m 75/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3135 - loss: 2.0592
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3131 - loss: 2.0581
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3124 - loss: 2.0579
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3125 - loss: 2.0571 - val_accuracy: 0.3748 - val_loss: 2.0561
Epoch 39/124

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[1m 24/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3195 - loss: 2.0209 
[1m 48/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3190 - loss: 2.0271
[1m 74/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3182 - loss: 2.0303
[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3173 - loss: 2.0323
[1m126/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3166 - loss: 2.0339
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3160 - loss: 2.0352 - val_accuracy: 0.3709 - val_loss: 2.0466
Epoch 40/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3130 - loss: 2.0329 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3071 - loss: 2.0459
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3065 - loss: 2.0480
[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3070 - loss: 2.0475
[1m129/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3076 - loss: 2.0459
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3082 - loss: 2.0445 - val_accuracy: 0.3776 - val_loss: 2.0405
Epoch 41/124

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[1m 96/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3168 - loss: 2.0201
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Epoch 42/124

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[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3296 - loss: 2.0026
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Epoch 43/124

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[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3198 - loss: 2.0181
[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3193 - loss: 2.0180
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3189 - loss: 2.0177
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Epoch 44/124

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[1m 47/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3230 - loss: 1.9941
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[1m 97/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3231 - loss: 1.9940
[1m122/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3241 - loss: 1.9933
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Epoch 45/124

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[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3283 - loss: 2.0159 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3372 - loss: 1.9964
[1m 75/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3405 - loss: 1.9891
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3420 - loss: 1.9848
[1m126/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3422 - loss: 1.9830
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3415 - loss: 1.9828 - val_accuracy: 0.3820 - val_loss: 2.0133
Epoch 46/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.3828 - loss: 1.9189
[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3371 - loss: 1.9485 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3310 - loss: 1.9682
[1m 75/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3285 - loss: 1.9734
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3275 - loss: 1.9759
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3273 - loss: 1.9771
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3272 - loss: 1.9781 - val_accuracy: 0.3859 - val_loss: 2.0246
Epoch 47/124

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[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3459 - loss: 1.9599
[1m128/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3442 - loss: 1.9627
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Epoch 48/124

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[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3325 - loss: 1.9640
[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3331 - loss: 1.9644
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Epoch 49/124

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[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3215 - loss: 1.9623
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[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3276 - loss: 1.9571
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Epoch 50/124

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[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3389 - loss: 1.9510
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[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3406 - loss: 1.9453
[1m120/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3407 - loss: 1.9451
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Epoch 51/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3623 - loss: 1.8991 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3582 - loss: 1.9152
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[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3535 - loss: 1.9274
[1m121/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3523 - loss: 1.9298
[1m144/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.3515 - loss: 1.9310
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Epoch 52/124

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[1m 24/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3444 - loss: 1.9383 
[1m 48/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3439 - loss: 1.9333
[1m 72/145[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3455 - loss: 1.9285
[1m 97/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3452 - loss: 1.9284
[1m123/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3445 - loss: 1.9288
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3443 - loss: 1.9292 - val_accuracy: 0.3929 - val_loss: 2.0115
Epoch 53/124

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[1m 23/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3584 - loss: 1.9269 
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[1m 94/145[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3612 - loss: 1.9237
[1m119/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3605 - loss: 1.9233
[1m143/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.3595 - loss: 1.9239
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3594 - loss: 1.9241 - val_accuracy: 0.3959 - val_loss: 2.0219
Epoch 54/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3579 - loss: 1.9278 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3583 - loss: 1.9259
[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3563 - loss: 1.9259
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Epoch 55/124

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Epoch 56/124

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[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3542 - loss: 1.8961
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Epoch 57/124

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[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3454 - loss: 1.9151
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[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3459 - loss: 1.9152
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3475 - loss: 1.9124
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Epoch 58/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3539 - loss: 1.8855 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3571 - loss: 1.8844
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[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3607 - loss: 1.8836
[1m122/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3610 - loss: 1.8840
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Epoch 59/124

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[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3669 - loss: 1.9328 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3622 - loss: 1.9268
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[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3610 - loss: 1.9147
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3615 - loss: 1.9097
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3621 - loss: 1.9057 - val_accuracy: 0.4066 - val_loss: 1.9945
Epoch 60/124

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[1m 48/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3660 - loss: 1.8806
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[1m 97/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3663 - loss: 1.8782
[1m121/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3662 - loss: 1.8773
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3662 - loss: 1.8763 - val_accuracy: 0.4006 - val_loss: 2.0164
Epoch 61/124

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Epoch 62/124

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Epoch 63/124

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Epoch 64/124

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[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3647 - loss: 1.8742
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Epoch 65/124

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[1m122/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3834 - loss: 1.8289
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3824 - loss: 1.8315
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Epoch 66/124

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[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3877 - loss: 1.8320 
[1m 48/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3839 - loss: 1.8352
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[1m 95/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3824 - loss: 1.8314
[1m121/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3814 - loss: 1.8321
[1m142/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.3808 - loss: 1.8330
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Epoch 67/124

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Epoch 68/124

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[1m144/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.3864 - loss: 1.8281
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[1m55/86[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 938us/step  
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Saved model to disk.

Accuracy capturado en la ejecución 25: 33.14 [%]
F1-score capturado en la ejecución 25: 32.66 [%]

=== EJECUCIÓN 26 ===

--- TRAIN (ejecución 26) ---

--- TEST (ejecución 26) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 409,231 (1.56 MB)
 Trainable params: 409,231 (1.56 MB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 6, 250)
(18469, 6, 250)

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[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 17ms/step
[1m63/86[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 812us/step
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step 
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 10ms/step

[1m  1/158[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step
[1m 58/158[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 885us/step
[1m127/158[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 798us/step
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 41.25 [%]
Global F1 score (validation) = 39.17 [%]
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[[0.0013234  0.0005136  0.00126444 ... 0.03014122 0.00256863 0.00060441]
 [0.00161787 0.00104734 0.00166317 ... 0.06510331 0.00321092 0.00061655]
 [0.00225528 0.00104482 0.00241331 ... 0.04468461 0.00355744 0.00086666]
 ...
 [0.17891416 0.04336265 0.21436693 ... 0.00038093 0.21437755 0.06737173]
 [0.24207236 0.11429361 0.16314816 ... 0.00096869 0.18290587 0.0414451 ]
 [0.19398308 0.04945766 0.20233196 ... 0.00039755 0.24708034 0.05293775]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 47.51 [%]
Global accuracy score (test) = 34.86 [%]
Global F1 score (train) = 47.29 [%]
Global F1 score (test) = 34.06 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.35      0.46      0.40       184
 CAMINAR CON MÓVIL O LIBRO       0.39      0.35      0.37       184
       CAMINAR USUAL SPEED       0.13      0.06      0.08       184
            CAMINAR ZIGZAG       0.20      0.23      0.21       184
          DE PIE BARRIENDO       0.28      0.20      0.23       184
   DE PIE DOBLANDO TOALLAS       0.31      0.28      0.29       184
    DE PIE MOVIENDO LIBROS       0.41      0.25      0.31       184
          DE PIE USANDO PC       0.26      0.28      0.27       184
        FASE REPOSO CON K5       0.44      0.74      0.55       184
INCREMENTAL CICLOERGOMETRO       0.58      0.62      0.60       184
           SENTADO LEYENDO       0.42      0.39      0.40       184
         SENTADO USANDO PC       0.17      0.14      0.15       184
      SENTADO VIENDO LA TV       0.42      0.36      0.39       184
   SUBIR Y BAJAR ESCALERAS       0.21      0.33      0.26       184
                    TROTAR       0.61      0.57      0.59       161

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

2025-10-28 13:07:03.718778: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-28 13:07:03.729962: 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 00:00:1761653223.743045 1910103 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761653223.747163 1910103 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761653223.756969 1910103 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761653223.756989 1910103 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761653223.756991 1910103 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761653223.756993 1910103 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-28 13:07:03.760197: 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 00:00:1761653226.121229 1910103 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13749 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 409,231 (1.56 MB)
 Trainable params: 409,231 (1.56 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/124
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761653228.687074 1910237 service.cc:152] XLA service 0x7a40540120b0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761653228.687136 1910237 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-28 13:07:08.743415: 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 00:00:1761653229.048945 1910237 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761653232.803859 1910237 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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
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Epoch 2/124

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Epoch 3/124

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Epoch 4/124

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[1m143/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.1375 - loss: 2.7668
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Epoch 5/124

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[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1489 - loss: 2.7036
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Epoch 6/124

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[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1596 - loss: 2.6375
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Epoch 7/124

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Epoch 8/124

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Epoch 9/124

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[1m120/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1857 - loss: 2.5189
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[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.1856 - loss: 2.5191 - val_accuracy: 0.2996 - val_loss: 2.2871
Epoch 10/124

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[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1931 - loss: 2.4764 
[1m 48/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1946 - loss: 2.4785
[1m 71/145[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1960 - loss: 2.4763
[1m 94/145[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1962 - loss: 2.4762
[1m120/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1965 - loss: 2.4758
[1m144/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.1963 - loss: 2.4759
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.1963 - loss: 2.4759 - val_accuracy: 0.3216 - val_loss: 2.2770
Epoch 11/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.2344 - loss: 2.3865
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2077 - loss: 2.4615 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2040 - loss: 2.4650
[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2025 - loss: 2.4650
[1m105/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2021 - loss: 2.4640
[1m129/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2021 - loss: 2.4631
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2022 - loss: 2.4621 - val_accuracy: 0.3407 - val_loss: 2.2546
Epoch 12/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.1953 - loss: 2.4169
[1m 24/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1994 - loss: 2.4187 
[1m 47/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1988 - loss: 2.4226
[1m 72/145[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1981 - loss: 2.4252
[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1982 - loss: 2.4256
[1m123/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1980 - loss: 2.4255
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.1980 - loss: 2.4248 - val_accuracy: 0.3238 - val_loss: 2.2490
Epoch 13/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.2188 - loss: 2.3689
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2017 - loss: 2.4364 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2040 - loss: 2.4361
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2052 - loss: 2.4343
[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2056 - loss: 2.4324
[1m128/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2061 - loss: 2.4293
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2067 - loss: 2.4268 - val_accuracy: 0.3353 - val_loss: 2.2332
Epoch 14/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2204 - loss: 2.3983 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2169 - loss: 2.3951
[1m 72/145[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2167 - loss: 2.3919
[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2164 - loss: 2.3895
[1m123/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2162 - loss: 2.3874
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2164 - loss: 2.3861 - val_accuracy: 0.3512 - val_loss: 2.2160
Epoch 15/124

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[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2372 - loss: 2.3465 
[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2303 - loss: 2.3622
[1m 74/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2281 - loss: 2.3645
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2270 - loss: 2.3649
[1m126/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2269 - loss: 2.3635
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2269 - loss: 2.3628 - val_accuracy: 0.3446 - val_loss: 2.2058
Epoch 16/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2071 - loss: 2.3845 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2109 - loss: 2.3723
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2148 - loss: 2.3651
[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2173 - loss: 2.3589
[1m127/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2195 - loss: 2.3545
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2208 - loss: 2.3521 - val_accuracy: 0.3566 - val_loss: 2.1899
Epoch 17/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.3047 - loss: 2.2129
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2508 - loss: 2.3017 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2438 - loss: 2.3174
[1m 75/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2410 - loss: 2.3222
[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2400 - loss: 2.3225
[1m121/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2394 - loss: 2.3218
[1m143/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.2392 - loss: 2.3213
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2392 - loss: 2.3212 - val_accuracy: 0.3572 - val_loss: 2.1850
Epoch 18/124

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[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2544 - loss: 2.2613 
[1m 48/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2492 - loss: 2.2766
[1m 73/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2463 - loss: 2.2850
[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2451 - loss: 2.2898
[1m122/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2448 - loss: 2.2919
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2445 - loss: 2.2933 - val_accuracy: 0.3550 - val_loss: 2.1651
Epoch 19/124

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[1m 28/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2431 - loss: 2.2854 
[1m 54/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2479 - loss: 2.2832
[1m 79/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2484 - loss: 2.2828
[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2484 - loss: 2.2825
[1m128/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2480 - loss: 2.2824
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2477 - loss: 2.2825 - val_accuracy: 0.3669 - val_loss: 2.1566
Epoch 20/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.2031 - loss: 2.3530
[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2515 - loss: 2.2839 
[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2484 - loss: 2.2837
[1m 73/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2472 - loss: 2.2817
[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2466 - loss: 2.2795
[1m123/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2466 - loss: 2.2784
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2468 - loss: 2.2779 - val_accuracy: 0.3566 - val_loss: 2.1511
Epoch 21/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2702 - loss: 2.2327 
[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2635 - loss: 2.2423
[1m 74/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2611 - loss: 2.2453
[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2599 - loss: 2.2459
[1m123/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2594 - loss: 2.2455
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2593 - loss: 2.2448 - val_accuracy: 0.3534 - val_loss: 2.1467
Epoch 22/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2458 - loss: 2.2645 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2512 - loss: 2.2542
[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2542 - loss: 2.2499
[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2559 - loss: 2.2470
[1m126/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2570 - loss: 2.2447
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2577 - loss: 2.2430 - val_accuracy: 0.3633 - val_loss: 2.1366
Epoch 23/124

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[1m 28/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2622 - loss: 2.2471 
[1m 54/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2643 - loss: 2.2383
[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2640 - loss: 2.2344
[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2641 - loss: 2.2307
[1m128/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2642 - loss: 2.2280
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2647 - loss: 2.2260 - val_accuracy: 0.3613 - val_loss: 2.1314
Epoch 24/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 26ms/step - accuracy: 0.2344 - loss: 2.2193
[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2552 - loss: 2.2289 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2587 - loss: 2.2232
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2617 - loss: 2.2173
[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2636 - loss: 2.2150
[1m126/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2650 - loss: 2.2128
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2658 - loss: 2.2117 - val_accuracy: 0.3716 - val_loss: 2.1173
Epoch 25/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2344 - loss: 2.2008
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2555 - loss: 2.2241 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2613 - loss: 2.2149
[1m 79/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2652 - loss: 2.2095
[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2676 - loss: 2.2056
[1m129/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2691 - loss: 2.2022
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2696 - loss: 2.2008 - val_accuracy: 0.3653 - val_loss: 2.1240
Epoch 26/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.3047 - loss: 2.2466
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2857 - loss: 2.1876 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2802 - loss: 2.1858
[1m 75/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2782 - loss: 2.1837
[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2771 - loss: 2.1837
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2766 - loss: 2.1837
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2765 - loss: 2.1834 - val_accuracy: 0.3701 - val_loss: 2.1103
Epoch 27/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.2031 - loss: 2.2880
[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2660 - loss: 2.2053 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2735 - loss: 2.1922
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2754 - loss: 2.1886
[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2766 - loss: 2.1864
[1m126/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2774 - loss: 2.1843
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2779 - loss: 2.1830 - val_accuracy: 0.3705 - val_loss: 2.1043
Epoch 28/124

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[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2744 - loss: 2.1745 
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[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2797 - loss: 2.1604
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2798 - loss: 2.1595
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Epoch 29/124

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Epoch 30/124

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[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2927 - loss: 2.1483 
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[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2917 - loss: 2.1322
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Epoch 31/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2865 - loss: 2.1049 
[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2893 - loss: 2.1064
[1m 75/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2901 - loss: 2.1106
[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2908 - loss: 2.1126
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2910 - loss: 2.1136
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2913 - loss: 2.1139 - val_accuracy: 0.3812 - val_loss: 2.0594
Epoch 32/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3026 - loss: 2.1209 
[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2996 - loss: 2.1171
[1m 73/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2996 - loss: 2.1118
[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2999 - loss: 2.1067
[1m122/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3003 - loss: 2.1049
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3004 - loss: 2.1044
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3004 - loss: 2.1044 - val_accuracy: 0.3671 - val_loss: 2.0914
Epoch 33/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2943 - loss: 2.0945 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2993 - loss: 2.0897
[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3023 - loss: 2.0877
[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3026 - loss: 2.0888
[1m128/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3020 - loss: 2.0906
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3018 - loss: 2.0914 - val_accuracy: 0.3750 - val_loss: 2.0756
Epoch 34/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3008 - loss: 2.0983 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3027 - loss: 2.0938
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3032 - loss: 2.0898
[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3038 - loss: 2.0879
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3044 - loss: 2.0864
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3045 - loss: 2.0857 - val_accuracy: 0.3814 - val_loss: 2.0470
Epoch 35/124

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[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2986 - loss: 2.0932
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[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2999 - loss: 2.0912
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Epoch 36/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3196 - loss: 2.0520 
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[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3145 - loss: 2.0627
[1m104/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3132 - loss: 2.0643
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Epoch 37/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3153 - loss: 2.0713 
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[1m 79/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3121 - loss: 2.0671
[1m106/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3114 - loss: 2.0660
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Epoch 38/124

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[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3188 - loss: 2.0549
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3182 - loss: 2.0542
[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3174 - loss: 2.0540
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[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3168 - loss: 2.0534 - val_accuracy: 0.3816 - val_loss: 2.0154
Epoch 39/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3086 - loss: 2.0489 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3112 - loss: 2.0445
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3123 - loss: 2.0424
[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3122 - loss: 2.0420
[1m126/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3130 - loss: 2.0399
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3134 - loss: 2.0389 - val_accuracy: 0.3836 - val_loss: 2.0288
Epoch 40/124

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[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3206 - loss: 2.0270 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3177 - loss: 2.0258
[1m 74/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3188 - loss: 2.0218
[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3186 - loss: 2.0215
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3179 - loss: 2.0227
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3178 - loss: 2.0229 - val_accuracy: 0.3780 - val_loss: 2.0414
Epoch 41/124

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[1m 24/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3150 - loss: 2.0243 
[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3160 - loss: 2.0252
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[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3178 - loss: 2.0202
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3184 - loss: 2.0187
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3188 - loss: 2.0175 - val_accuracy: 0.3778 - val_loss: 2.0132
Epoch 42/124

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[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3211 - loss: 2.0081
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Epoch 43/124

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[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3214 - loss: 1.9919
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Epoch 44/124

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[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3302 - loss: 1.9807
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[1m 96/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3296 - loss: 1.9814
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Epoch 45/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.3281 - loss: 1.9195
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3152 - loss: 2.0038 
[1m 53/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3235 - loss: 1.9983
[1m 79/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3273 - loss: 1.9944
[1m104/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3293 - loss: 1.9918
[1m129/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3307 - loss: 1.9894
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Epoch 46/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3341 - loss: 1.9686 
[1m 53/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3329 - loss: 1.9632
[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3321 - loss: 1.9647
[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3326 - loss: 1.9652
[1m127/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3334 - loss: 1.9651
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3339 - loss: 1.9647 - val_accuracy: 0.3879 - val_loss: 2.0020
Epoch 47/124

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[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3311 - loss: 1.9676 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3321 - loss: 1.9653
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3318 - loss: 1.9660
[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3316 - loss: 1.9668
[1m127/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3317 - loss: 1.9668
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3317 - loss: 1.9665 - val_accuracy: 0.3851 - val_loss: 2.0135
Epoch 48/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3455 - loss: 1.9606 
[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3439 - loss: 1.9567
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[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3423 - loss: 1.9557
[1m123/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3419 - loss: 1.9551
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Epoch 49/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3318 - loss: 1.9437 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3344 - loss: 1.9425
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3353 - loss: 1.9437
[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3352 - loss: 1.9466
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Epoch 50/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3462 - loss: 1.9029 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3433 - loss: 1.9203
[1m 73/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3425 - loss: 1.9258
[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3426 - loss: 1.9292
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3429 - loss: 1.9311
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Epoch 51/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3352 - loss: 1.9717 
[1m 54/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3389 - loss: 1.9512
[1m 81/145[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3402 - loss: 1.9436
[1m105/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3415 - loss: 1.9394
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[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3432 - loss: 1.9342 - val_accuracy: 0.4000 - val_loss: 1.9637
Epoch 52/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3236 - loss: 1.9500 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3324 - loss: 1.9369
[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3372 - loss: 1.9324
[1m104/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3393 - loss: 1.9308
[1m130/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3404 - loss: 1.9306
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3407 - loss: 1.9305 - val_accuracy: 0.3994 - val_loss: 1.9925
Epoch 53/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3485 - loss: 1.9174 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3497 - loss: 1.9164
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3510 - loss: 1.9171
[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3510 - loss: 1.9174
[1m126/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3507 - loss: 1.9182
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3504 - loss: 1.9189 - val_accuracy: 0.3915 - val_loss: 1.9753
Epoch 54/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.3750 - loss: 1.8053
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3581 - loss: 1.8792 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3545 - loss: 1.8899
[1m 75/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3520 - loss: 1.8977
[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3510 - loss: 1.9016
[1m126/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3509 - loss: 1.9027
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3511 - loss: 1.9030 - val_accuracy: 0.4034 - val_loss: 1.9642
Epoch 55/124

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[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3548 - loss: 1.9074 
[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3576 - loss: 1.9010
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3590 - loss: 1.8969
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3590 - loss: 1.8965
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3588 - loss: 1.8968
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3587 - loss: 1.8975 - val_accuracy: 0.4080 - val_loss: 1.9773
Epoch 56/124

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[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3659 - loss: 1.8729 
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[1m123/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3593 - loss: 1.8935
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3588 - loss: 1.8940 - val_accuracy: 0.4042 - val_loss: 1.9882

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[1m61/86[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 853us/step  
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step 
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Saved model to disk.

Accuracy capturado en la ejecución 26: 34.86 [%]
F1-score capturado en la ejecución 26: 34.06 [%]

=== EJECUCIÓN 27 ===

--- TRAIN (ejecución 27) ---

--- TEST (ejecución 27) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 409,231 (1.56 MB)
 Trainable params: 409,231 (1.56 MB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 6, 250)
(18469, 6, 250)

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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
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[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 17ms/step
[1m61/86[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 845us/step
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step  
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 9ms/step

[1m  1/158[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 17ms/step
[1m 61/158[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 845us/step
[1m120/158[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 850us/step
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 40.42 [%]
Global F1 score (validation) = 38.17 [%]
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[[0.00152149 0.00070695 0.00140644 ... 0.03326437 0.00258072 0.00073242]
 [0.00258565 0.00226422 0.00238053 ... 0.07856636 0.0051471  0.00089272]
 [0.00407577 0.00182107 0.00373872 ... 0.0687017  0.00703407 0.00220365]
 ...
 [0.17190668 0.04596142 0.23529956 ... 0.00031954 0.22638996 0.09358171]
 [0.18302968 0.14548104 0.13980845 ... 0.00290339 0.09162012 0.05078087]
 [0.20355462 0.04635141 0.19503467 ... 0.00057531 0.25710124 0.06693943]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 42.4 [%]
Global accuracy score (test) = 33.91 [%]
Global F1 score (train) = 41.24 [%]
Global F1 score (test) = 33.15 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.31      0.38      0.34       184
 CAMINAR CON MÓVIL O LIBRO       0.35      0.35      0.35       184
       CAMINAR USUAL SPEED       0.22      0.15      0.18       184
            CAMINAR ZIGZAG       0.18      0.07      0.10       184
          DE PIE BARRIENDO       0.27      0.30      0.29       184
   DE PIE DOBLANDO TOALLAS       0.36      0.33      0.34       184
    DE PIE MOVIENDO LIBROS       0.35      0.22      0.27       184
          DE PIE USANDO PC       0.25      0.27      0.26       184
        FASE REPOSO CON K5       0.45      0.74      0.56       184
INCREMENTAL CICLOERGOMETRO       0.48      0.64      0.55       184
           SENTADO LEYENDO       0.44      0.37      0.40       184
         SENTADO USANDO PC       0.14      0.07      0.09       184
      SENTADO VIENDO LA TV       0.39      0.28      0.32       184
   SUBIR Y BAJAR ESCALERAS       0.17      0.39      0.24       184
                    TROTAR       0.86      0.55      0.67       161

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

2025-10-28 13:07:55.767835: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-28 13:07:55.779091: 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 00:00:1761653275.792385 1916357 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761653275.796644 1916357 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761653275.806473 1916357 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761653275.806496 1916357 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761653275.806499 1916357 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761653275.806501 1916357 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-28 13:07:55.809772: 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 00:00:1761653278.150510 1916357 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13749 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 409,231 (1.56 MB)
 Trainable params: 409,231 (1.56 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/124
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761653280.674069 1916479 service.cc:152] XLA service 0x7392c80117d0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761653280.674130 1916479 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-28 13:08:00.732170: 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 00:00:1761653281.026475 1916479 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761653284.637459 1916479 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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
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Epoch 2/124

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Epoch 3/124

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Epoch 4/124

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[1m109/145[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1302 - loss: 2.7807
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Epoch 5/124

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[1m104/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1347 - loss: 2.7282
[1m129/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1364 - loss: 2.7254
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.1375 - loss: 2.7235 - val_accuracy: 0.2184 - val_loss: 2.3994
Epoch 6/124

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[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1541 - loss: 2.6684
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[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.1539 - loss: 2.6656 - val_accuracy: 0.2515 - val_loss: 2.3344
Epoch 7/124

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Epoch 8/124

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[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1608 - loss: 2.5825
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Epoch 9/124

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[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1750 - loss: 2.5394
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Epoch 10/124

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[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1828 - loss: 2.5168
[1m105/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1833 - loss: 2.5148
[1m129/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1839 - loss: 2.5130
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.1841 - loss: 2.5122 - val_accuracy: 0.2920 - val_loss: 2.2522
Epoch 11/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1894 - loss: 2.4997 
[1m 53/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1923 - loss: 2.4864
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1933 - loss: 2.4803
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1944 - loss: 2.4765
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1954 - loss: 2.4733
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.1957 - loss: 2.4720 - val_accuracy: 0.2978 - val_loss: 2.2325
Epoch 12/124

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[1m 21/145[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.1917 - loss: 2.4670 
[1m 46/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1958 - loss: 2.4535
[1m 71/145[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1973 - loss: 2.4459
[1m 97/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1973 - loss: 2.4427
[1m122/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1971 - loss: 2.4407
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.1971 - loss: 2.4395 - val_accuracy: 0.3095 - val_loss: 2.2248
Epoch 13/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.1328 - loss: 2.6467
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1910 - loss: 2.4554 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1946 - loss: 2.4466
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1960 - loss: 2.4441
[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1972 - loss: 2.4423
[1m126/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1984 - loss: 2.4388
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.1994 - loss: 2.4360 - val_accuracy: 0.3206 - val_loss: 2.2188
Epoch 14/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2734 - loss: 2.2800
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[1m126/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2148 - loss: 2.3781
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Epoch 15/124

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Epoch 16/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2344 - loss: 2.3379 
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[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2318 - loss: 2.3327
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Epoch 17/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2190 - loss: 2.3461 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2189 - loss: 2.3453
[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2205 - loss: 2.3422
[1m104/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2216 - loss: 2.3399
[1m130/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2227 - loss: 2.3379
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2232 - loss: 2.3369 - val_accuracy: 0.3383 - val_loss: 2.1929
Epoch 18/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2477 - loss: 2.3094 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2469 - loss: 2.3069
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2466 - loss: 2.3032
[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2457 - loss: 2.3009
[1m128/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2449 - loss: 2.2995
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2443 - loss: 2.2988 - val_accuracy: 0.3417 - val_loss: 2.1782
Epoch 19/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.3203 - loss: 2.1434
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2454 - loss: 2.2553 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2447 - loss: 2.2641
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2442 - loss: 2.2669
[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2440 - loss: 2.2690
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2441 - loss: 2.2700
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2442 - loss: 2.2707 - val_accuracy: 0.3437 - val_loss: 2.1700
Epoch 20/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2422 - loss: 2.3795
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2495 - loss: 2.2779 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2488 - loss: 2.2721
[1m 75/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2491 - loss: 2.2703
[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2494 - loss: 2.2683
[1m127/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2499 - loss: 2.2672
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2500 - loss: 2.2669 - val_accuracy: 0.3397 - val_loss: 2.1712
Epoch 21/124

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[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2387 - loss: 2.2902 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2428 - loss: 2.2748
[1m 75/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2448 - loss: 2.2687
[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2466 - loss: 2.2629
[1m127/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2483 - loss: 2.2585
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2490 - loss: 2.2568 - val_accuracy: 0.3425 - val_loss: 2.1478
Epoch 22/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2266 - loss: 2.2838
[1m 28/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2510 - loss: 2.2697 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2563 - loss: 2.2598
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2586 - loss: 2.2519
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2599 - loss: 2.2471
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2606 - loss: 2.2436
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2608 - loss: 2.2415 - val_accuracy: 0.3476 - val_loss: 2.1596
Epoch 23/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2500 - loss: 2.2244
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2619 - loss: 2.1991 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2589 - loss: 2.2105
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2576 - loss: 2.2155
[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2581 - loss: 2.2177
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2590 - loss: 2.2174
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2594 - loss: 2.2176 - val_accuracy: 0.3514 - val_loss: 2.1306
Epoch 24/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.3125 - loss: 2.2066
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2818 - loss: 2.1910 
[1m 53/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2746 - loss: 2.1982
[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2719 - loss: 2.1997
[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2711 - loss: 2.1997
[1m128/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2704 - loss: 2.2002
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2700 - loss: 2.2008 - val_accuracy: 0.3494 - val_loss: 2.1256
Epoch 25/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2812 - loss: 2.1886
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2625 - loss: 2.1925 
[1m 48/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2689 - loss: 2.1879
[1m 71/145[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2715 - loss: 2.1844
[1m 94/145[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2725 - loss: 2.1831
[1m117/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2730 - loss: 2.1831
[1m142/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.2736 - loss: 2.1827
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2737 - loss: 2.1826 - val_accuracy: 0.3466 - val_loss: 2.1378
Epoch 26/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.3359 - loss: 2.0027
[1m 24/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2764 - loss: 2.1826 
[1m 47/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2793 - loss: 2.1792
[1m 72/145[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2801 - loss: 2.1761
[1m 96/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2800 - loss: 2.1765
[1m121/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2802 - loss: 2.1757
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2798 - loss: 2.1758 - val_accuracy: 0.3492 - val_loss: 2.1318
Epoch 27/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.3281 - loss: 2.1118
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3168 - loss: 2.1369 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3041 - loss: 2.1474
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2981 - loss: 2.1515
[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2942 - loss: 2.1549
[1m123/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2915 - loss: 2.1574
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2899 - loss: 2.1588 - val_accuracy: 0.3554 - val_loss: 2.1175
Epoch 28/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.2734 - loss: 2.2195
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2785 - loss: 2.1382 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2762 - loss: 2.1471
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[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2766 - loss: 2.1488
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[1m144/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.2768 - loss: 2.1509
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Epoch 29/124

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[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2522 - loss: 2.1896 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2643 - loss: 2.1662
[1m 73/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2701 - loss: 2.1566
[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2738 - loss: 2.1520
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2765 - loss: 2.1486
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2781 - loss: 2.1467 - val_accuracy: 0.3518 - val_loss: 2.1157
Epoch 30/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2901 - loss: 2.1164 
[1m 54/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2875 - loss: 2.1220
[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2870 - loss: 2.1224
[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2863 - loss: 2.1235
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2861 - loss: 2.1239
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2864 - loss: 2.1233 - val_accuracy: 0.3635 - val_loss: 2.1272
Epoch 31/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2974 - loss: 2.1081 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2961 - loss: 2.1101
[1m 74/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2955 - loss: 2.1088
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2942 - loss: 2.1095
[1m126/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2936 - loss: 2.1105
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2931 - loss: 2.1111 - val_accuracy: 0.3651 - val_loss: 2.0952
Epoch 32/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2578 - loss: 2.2690
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2852 - loss: 2.1309 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2880 - loss: 2.1261
[1m 75/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2895 - loss: 2.1211
[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2900 - loss: 2.1190
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2907 - loss: 2.1166
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2912 - loss: 2.1149 - val_accuracy: 0.3619 - val_loss: 2.0909
Epoch 33/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.3984 - loss: 1.9990
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3342 - loss: 2.0616 
[1m 53/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3219 - loss: 2.0735
[1m 80/145[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3150 - loss: 2.0809
[1m105/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3116 - loss: 2.0851
[1m129/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3092 - loss: 2.0875
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3079 - loss: 2.0887 - val_accuracy: 0.3649 - val_loss: 2.0831
Epoch 34/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2812 - loss: 2.1201
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2980 - loss: 2.0942 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3005 - loss: 2.0837
[1m 79/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2999 - loss: 2.0797
[1m105/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3000 - loss: 2.0772
[1m130/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3002 - loss: 2.0760
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3002 - loss: 2.0761 - val_accuracy: 0.3715 - val_loss: 2.0729
Epoch 35/124

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Epoch 36/124

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[1m106/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3069 - loss: 2.0810
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Epoch 37/124

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[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3148 - loss: 2.0275
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3142 - loss: 2.0302
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Epoch 38/124

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[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3226 - loss: 1.9963
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3204 - loss: 2.0029
[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3181 - loss: 2.0101
[1m127/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3168 - loss: 2.0154
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3163 - loss: 2.0183 - val_accuracy: 0.3653 - val_loss: 2.0670
Epoch 39/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2949 - loss: 2.0487 
[1m 53/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3005 - loss: 2.0501
[1m 80/145[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3037 - loss: 2.0473
[1m106/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3058 - loss: 2.0448
[1m131/145[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.3071 - loss: 2.0425
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3076 - loss: 2.0417 - val_accuracy: 0.3715 - val_loss: 2.0499
Epoch 40/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.2891 - loss: 2.0992
[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3000 - loss: 2.0445 
[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3037 - loss: 2.0388
[1m 73/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3063 - loss: 2.0364
[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3083 - loss: 2.0349
[1m123/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3099 - loss: 2.0337
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3109 - loss: 2.0325 - val_accuracy: 0.3812 - val_loss: 2.0558
Epoch 41/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.3750 - loss: 1.8626
[1m 24/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3484 - loss: 1.9635 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3392 - loss: 1.9788
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3353 - loss: 1.9883
[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3331 - loss: 1.9943
[1m129/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3320 - loss: 1.9974
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3316 - loss: 1.9987 - val_accuracy: 0.3802 - val_loss: 2.0430
Epoch 42/124

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[1m 79/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3245 - loss: 1.9878
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Epoch 43/124

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[1m105/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3208 - loss: 1.9934
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Epoch 44/124

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[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3324 - loss: 1.9630
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[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3311 - loss: 1.9712
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Epoch 45/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3334 - loss: 1.9474 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3336 - loss: 1.9615
[1m 75/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3338 - loss: 1.9662
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3337 - loss: 1.9689
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3338 - loss: 1.9702
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3341 - loss: 1.9709 - val_accuracy: 0.3728 - val_loss: 2.0290
Epoch 46/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.3438 - loss: 1.9241
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3311 - loss: 1.9551 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3321 - loss: 1.9589
[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3322 - loss: 1.9619
[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3329 - loss: 1.9622
[1m128/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3336 - loss: 1.9629
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3340 - loss: 1.9634 - val_accuracy: 0.3905 - val_loss: 2.0102
Epoch 47/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.3828 - loss: 1.9545
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3338 - loss: 1.9755 
[1m 54/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3419 - loss: 1.9528
[1m 80/145[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3426 - loss: 1.9500
[1m106/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3422 - loss: 1.9499
[1m132/145[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.3421 - loss: 1.9498
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3418 - loss: 1.9501 - val_accuracy: 0.3792 - val_loss: 2.0155
Epoch 48/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.3281 - loss: 1.9750
[1m 24/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3404 - loss: 1.9468 
[1m 46/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3414 - loss: 1.9468
[1m 72/145[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3411 - loss: 1.9470
[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3411 - loss: 1.9454
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3414 - loss: 1.9440
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3416 - loss: 1.9430 - val_accuracy: 0.3861 - val_loss: 1.9984
Epoch 49/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.3438 - loss: 1.8795
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3576 - loss: 1.9099 
[1m 53/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3505 - loss: 1.9195
[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3463 - loss: 1.9254
[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3437 - loss: 1.9303
[1m127/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3422 - loss: 1.9345
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3415 - loss: 1.9368 - val_accuracy: 0.3879 - val_loss: 2.0177
Epoch 50/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2969 - loss: 1.9459
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3428 - loss: 1.9507 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3468 - loss: 1.9445
[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3483 - loss: 1.9405
[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3487 - loss: 1.9384
[1m128/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3485 - loss: 1.9378
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3483 - loss: 1.9380 - val_accuracy: 0.3842 - val_loss: 2.0319
Epoch 51/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.3516 - loss: 2.0214
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3499 - loss: 1.9421 
[1m 54/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3527 - loss: 1.9363
[1m 80/145[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3525 - loss: 1.9354
[1m106/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3519 - loss: 1.9347
[1m132/145[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.3516 - loss: 1.9344
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3516 - loss: 1.9341 - val_accuracy: 0.3844 - val_loss: 2.0111
Epoch 52/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.3047 - loss: 1.9110
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3290 - loss: 1.9559 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3396 - loss: 1.9420
[1m 74/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3433 - loss: 1.9378
[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3452 - loss: 1.9344
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3463 - loss: 1.9317
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3471 - loss: 1.9298 - val_accuracy: 0.3836 - val_loss: 2.0333
Epoch 53/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.3438 - loss: 1.9427
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3452 - loss: 1.9262 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3488 - loss: 1.9170
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3493 - loss: 1.9136
[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3496 - loss: 1.9129
[1m126/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3496 - loss: 1.9129
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3498 - loss: 1.9134 - val_accuracy: 0.3863 - val_loss: 2.0255

[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:12[0m 859ms/step
[1m60/86[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 858us/step  
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step 
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 11ms/step
Saved model to disk.

Accuracy capturado en la ejecución 27: 33.91 [%]
F1-score capturado en la ejecución 27: 33.15 [%]

=== EJECUCIÓN 28 ===

--- TRAIN (ejecución 28) ---

--- TEST (ejecución 28) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 409,231 (1.56 MB)
 Trainable params: 409,231 (1.56 MB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 6, 250)
(18469, 6, 250)

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[1m 59/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 867us/step
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[1m187/578[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 809us/step
[1m251/578[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 804us/step
[1m317/578[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 796us/step
[1m385/578[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 787us/step
[1m451/578[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 784us/step
[1m516/578[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 783us/step
[1m576/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 789us/step
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
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[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m57/86[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 901us/step
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step  
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 9ms/step

[1m  1/158[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step
[1m 56/158[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 916us/step
[1m118/158[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 864us/step
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 38.63 [%]
Global F1 score (validation) = 36.47 [%]
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[[3.4947696e-03 2.0687636e-03 2.2491526e-03 ... 2.9628597e-02
  4.0834388e-03 6.4407632e-04]
 [2.1211063e-03 1.4678064e-03 1.9531271e-03 ... 5.9271749e-02
  3.1135280e-03 4.7169582e-04]
 [8.0861733e-04 2.9121939e-04 5.8180175e-04 ... 1.5017218e-02
  9.9374633e-04 2.1325500e-04]
 ...
 [1.8118012e-01 4.2123433e-02 2.2174576e-01 ... 3.7049907e-04
  2.4494895e-01 7.7343166e-02]
 [1.8510032e-01 5.8957145e-02 2.1758936e-01 ... 5.1899551e-04
  2.2287016e-01 9.0810299e-02]
 [2.0100042e-01 5.4598443e-02 2.0423919e-01 ... 6.1107409e-04
  2.4595800e-01 5.9468355e-02]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 42.2 [%]
Global accuracy score (test) = 32.15 [%]
Global F1 score (train) = 41.25 [%]
Global F1 score (test) = 31.41 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.25      0.27      0.26       184
 CAMINAR CON MÓVIL O LIBRO       0.36      0.34      0.35       184
       CAMINAR USUAL SPEED       0.09      0.05      0.06       184
            CAMINAR ZIGZAG       0.12      0.08      0.09       184
          DE PIE BARRIENDO       0.26      0.16      0.20       184
   DE PIE DOBLANDO TOALLAS       0.32      0.38      0.34       184
    DE PIE MOVIENDO LIBROS       0.36      0.22      0.27       184
          DE PIE USANDO PC       0.24      0.24      0.24       184
        FASE REPOSO CON K5       0.41      0.74      0.53       184
INCREMENTAL CICLOERGOMETRO       0.52      0.61      0.56       184
           SENTADO LEYENDO       0.44      0.33      0.38       184
         SENTADO USANDO PC       0.13      0.10      0.12       184
      SENTADO VIENDO LA TV       0.48      0.39      0.43       184
   SUBIR Y BAJAR ESCALERAS       0.16      0.39      0.23       184
                    TROTAR       0.75      0.56      0.64       161

                  accuracy                           0.32      2737
                 macro avg       0.33      0.32      0.31      2737
              weighted avg       0.32      0.32      0.31      2737

2025-10-28 13:08:46.301626: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-28 13:08:46.312937: 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 00:00:1761653326.325983 1922341 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761653326.330181 1922341 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761653326.339902 1922341 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761653326.339929 1922341 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761653326.339932 1922341 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761653326.339933 1922341 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-28 13:08:46.343151: 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 00:00:1761653328.671420 1922341 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13749 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 409,231 (1.56 MB)
 Trainable params: 409,231 (1.56 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/124
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761653331.226659 1922464 service.cc:152] XLA service 0x70cea4012950 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761653331.226724 1922464 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-28 13:08:51.283722: 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 00:00:1761653331.583955 1922464 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761653335.133734 1922464 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13:41[0m 6s/step - accuracy: 0.0469 - loss: 3.2762
[1m 23/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.0579 - loss: 3.2314  
[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.0623 - loss: 3.2197
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[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.0695 - loss: 3.1883
[1m126/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.0721 - loss: 3.1745
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 26ms/step - accuracy: 0.0738 - loss: 3.16522025-10-28 13:09:00.789689: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_316', 12 bytes spill stores, 12 bytes spill loads


[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m12s[0m 43ms/step - accuracy: 0.0738 - loss: 3.1647 - val_accuracy: 0.1656 - val_loss: 2.4451
Epoch 2/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1014 - loss: 2.9797 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1026 - loss: 2.9749
[1m 79/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1038 - loss: 2.9681
[1m107/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1048 - loss: 2.9615
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[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.1058 - loss: 2.9542 - val_accuracy: 0.2001 - val_loss: 2.4375
Epoch 3/124

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Epoch 4/124

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Epoch 5/124

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Epoch 6/124

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[1m104/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1528 - loss: 2.6611
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Epoch 7/124

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[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1584 - loss: 2.6106
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1586 - loss: 2.6094
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.1588 - loss: 2.6079 - val_accuracy: 0.2644 - val_loss: 2.3099
Epoch 8/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1663 - loss: 2.5778 
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[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1697 - loss: 2.5736
[1m126/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1706 - loss: 2.5712
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.1713 - loss: 2.5691 - val_accuracy: 0.2787 - val_loss: 2.2866
Epoch 9/124

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[1m 48/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1844 - loss: 2.5089
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[1m 96/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1827 - loss: 2.5181
[1m118/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1820 - loss: 2.5202
[1m142/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.1816 - loss: 2.5217
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Epoch 10/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1889 - loss: 2.4929 
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[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1879 - loss: 2.4995
[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1878 - loss: 2.4994
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[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.1878 - loss: 2.4981 - val_accuracy: 0.3047 - val_loss: 2.2620
Epoch 11/124

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[1m 23/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1876 - loss: 2.5006 
[1m 48/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1935 - loss: 2.4809
[1m 75/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1952 - loss: 2.4737
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1955 - loss: 2.4706
[1m126/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1954 - loss: 2.4689
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Epoch 12/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2014 - loss: 2.4575 
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[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1999 - loss: 2.4472
[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1993 - loss: 2.4446
[1m127/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1992 - loss: 2.4435
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.1993 - loss: 2.4426 - val_accuracy: 0.3222 - val_loss: 2.2257
Epoch 13/124

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[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2147 - loss: 2.3901 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2135 - loss: 2.3985
[1m 79/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2130 - loss: 2.4010
[1m106/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2119 - loss: 2.4024
[1m132/145[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.2111 - loss: 2.4032
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2109 - loss: 2.4032 - val_accuracy: 0.3280 - val_loss: 2.2208
Epoch 14/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2236 - loss: 2.4007 
[1m 53/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2201 - loss: 2.3988
[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2192 - loss: 2.3970
[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2190 - loss: 2.3955
[1m126/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2185 - loss: 2.3945
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2182 - loss: 2.3933 - val_accuracy: 0.3498 - val_loss: 2.1972
Epoch 15/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.2812 - loss: 2.3165
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2250 - loss: 2.3883 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2236 - loss: 2.3833
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2220 - loss: 2.3810
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2208 - loss: 2.3798
[1m126/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2200 - loss: 2.3790
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2199 - loss: 2.3775 - val_accuracy: 0.3484 - val_loss: 2.2012
Epoch 16/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.2266 - loss: 2.3173
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2144 - loss: 2.3867 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2178 - loss: 2.3724
[1m 80/145[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2203 - loss: 2.3645
[1m106/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2219 - loss: 2.3606
[1m131/145[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.2230 - loss: 2.3581
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2234 - loss: 2.3569 - val_accuracy: 0.3441 - val_loss: 2.1866
Epoch 17/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2412 - loss: 2.2966 
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Epoch 18/124

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[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2395 - loss: 2.3222
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Epoch 19/124

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[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2416 - loss: 2.2825 
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[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2451 - loss: 2.2881
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Epoch 20/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2423 - loss: 2.2714 
[1m 53/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2424 - loss: 2.2734
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2427 - loss: 2.2720
[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2439 - loss: 2.2711
[1m126/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2452 - loss: 2.2699
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2459 - loss: 2.2690 - val_accuracy: 0.3574 - val_loss: 2.1559
Epoch 21/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.2734 - loss: 2.2825
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2576 - loss: 2.2603 
[1m 53/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2540 - loss: 2.2575
[1m 79/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2542 - loss: 2.2547
[1m104/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2548 - loss: 2.2546
[1m130/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2545 - loss: 2.2557
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2544 - loss: 2.2559 - val_accuracy: 0.3641 - val_loss: 2.1500
Epoch 22/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.3125 - loss: 2.0643
[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2816 - loss: 2.1892 
[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2755 - loss: 2.2068
[1m 73/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2719 - loss: 2.2158
[1m 95/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2697 - loss: 2.2207
[1m120/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2678 - loss: 2.2243
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2664 - loss: 2.2262 - val_accuracy: 0.3564 - val_loss: 2.1645
Epoch 23/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2344 - loss: 2.1799
[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2606 - loss: 2.2123 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2603 - loss: 2.2228
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2598 - loss: 2.2276
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2596 - loss: 2.2288
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2600 - loss: 2.2289
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2604 - loss: 2.2287 - val_accuracy: 0.3560 - val_loss: 2.1416
Epoch 24/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2582 - loss: 2.2150 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2669 - loss: 2.2050
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2690 - loss: 2.2046
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2697 - loss: 2.2059
[1m126/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2702 - loss: 2.2062
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2704 - loss: 2.2062 - val_accuracy: 0.3593 - val_loss: 2.1431
Epoch 25/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2188 - loss: 2.2990
[1m 28/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2583 - loss: 2.2228 
[1m 55/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2612 - loss: 2.2139
[1m 79/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2633 - loss: 2.2100
[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2644 - loss: 2.2075
[1m130/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2653 - loss: 2.2054
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2659 - loss: 2.2044 - val_accuracy: 0.3685 - val_loss: 2.1230
Epoch 26/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.2500 - loss: 2.2450
[1m 24/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2778 - loss: 2.1676 
[1m 47/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2804 - loss: 2.1696
[1m 72/145[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2797 - loss: 2.1718
[1m 96/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2796 - loss: 2.1726
[1m119/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2790 - loss: 2.1730
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2785 - loss: 2.1738 - val_accuracy: 0.3601 - val_loss: 2.1308
Epoch 27/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2734 - loss: 2.0596
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2892 - loss: 2.1495 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2823 - loss: 2.1616
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2783 - loss: 2.1675
[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2775 - loss: 2.1688
[1m128/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2773 - loss: 2.1693
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2772 - loss: 2.1696 - val_accuracy: 0.3697 - val_loss: 2.1131
Epoch 28/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 25ms/step - accuracy: 0.3281 - loss: 2.1394
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2795 - loss: 2.1826 
[1m 48/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2802 - loss: 2.1745
[1m 73/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2795 - loss: 2.1686
[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2794 - loss: 2.1648
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2790 - loss: 2.1636
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2787 - loss: 2.1629 - val_accuracy: 0.3641 - val_loss: 2.1009
Epoch 29/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.3047 - loss: 2.2734
[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2774 - loss: 2.1713 
[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2787 - loss: 2.1599
[1m 75/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2811 - loss: 2.1536
[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2825 - loss: 2.1516
[1m127/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2837 - loss: 2.1493
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2842 - loss: 2.1484 - val_accuracy: 0.3703 - val_loss: 2.0906
Epoch 30/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.3281 - loss: 2.1189
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3036 - loss: 2.1274 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2995 - loss: 2.1280
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2975 - loss: 2.1283
[1m104/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2956 - loss: 2.1291
[1m128/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2943 - loss: 2.1299
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2937 - loss: 2.1300 - val_accuracy: 0.3748 - val_loss: 2.0991
Epoch 31/124

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[1m 24/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2795 - loss: 2.1649 
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Epoch 32/124

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Epoch 33/124

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[1m 48/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3039 - loss: 2.0868
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[1m 97/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3033 - loss: 2.0877
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Epoch 34/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2770 - loss: 2.1399 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2888 - loss: 2.1148
[1m 75/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2931 - loss: 2.1064
[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2953 - loss: 2.1011
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2962 - loss: 2.0983
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2966 - loss: 2.0968 - val_accuracy: 0.3693 - val_loss: 2.0764
Epoch 35/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2908 - loss: 2.0874 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2955 - loss: 2.0847
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2979 - loss: 2.0835
[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2991 - loss: 2.0840
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2998 - loss: 2.0838
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3002 - loss: 2.0835 - val_accuracy: 0.3814 - val_loss: 2.0710
Epoch 36/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.3203 - loss: 2.0104
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3211 - loss: 2.0536 
[1m 53/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3170 - loss: 2.0571
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3146 - loss: 2.0590
[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3126 - loss: 2.0612
[1m128/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3112 - loss: 2.0626
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3104 - loss: 2.0634 - val_accuracy: 0.3697 - val_loss: 2.0562
Epoch 37/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.2812 - loss: 1.9838
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3072 - loss: 2.0351 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3080 - loss: 2.0412
[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3077 - loss: 2.0463
[1m104/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3080 - loss: 2.0486
[1m128/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3081 - loss: 2.0503
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3082 - loss: 2.0509 - val_accuracy: 0.3667 - val_loss: 2.0685
Epoch 38/124

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[1m 97/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3140 - loss: 2.0501
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Epoch 39/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3158 - loss: 2.0458 
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[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3143 - loss: 2.0407
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Epoch 40/124

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[1m 46/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3271 - loss: 2.0266
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[1m 93/145[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3243 - loss: 2.0266
[1m119/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3234 - loss: 2.0255
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Epoch 41/124

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[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3230 - loss: 1.9954 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3260 - loss: 1.9982
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3270 - loss: 1.9998
[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3272 - loss: 2.0016
[1m121/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3266 - loss: 2.0039
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Epoch 42/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3194 - loss: 1.9941 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3183 - loss: 1.9967
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3189 - loss: 1.9977
[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3194 - loss: 1.9995
[1m126/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3199 - loss: 2.0009
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3202 - loss: 2.0020 - val_accuracy: 0.3732 - val_loss: 2.0252
Epoch 43/124

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[1m 23/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3254 - loss: 2.0164 
[1m 46/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3257 - loss: 2.0094
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[1m 96/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3260 - loss: 2.0061
[1m121/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3251 - loss: 2.0060
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3245 - loss: 2.0062 - val_accuracy: 0.3786 - val_loss: 2.0259
Epoch 44/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3394 - loss: 1.9609 
[1m 53/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3351 - loss: 1.9658
[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3319 - loss: 1.9728
[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3308 - loss: 1.9759
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3304 - loss: 1.9777
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3302 - loss: 1.9794 - val_accuracy: 0.3736 - val_loss: 2.0211
Epoch 45/124

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[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3342 - loss: 1.9757
[1m 73/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3329 - loss: 1.9772
[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3320 - loss: 1.9782
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Epoch 46/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3268 - loss: 1.9644 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3309 - loss: 1.9605
[1m 75/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3327 - loss: 1.9601
[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3331 - loss: 1.9598
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3336 - loss: 1.9601
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Epoch 47/124

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[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3475 - loss: 1.9534 
[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3408 - loss: 1.9547
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[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3386 - loss: 1.9552
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Epoch 48/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3342 - loss: 1.9607 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3389 - loss: 1.9459
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3406 - loss: 1.9423
[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3413 - loss: 1.9424
[1m123/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3410 - loss: 1.9443
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3402 - loss: 1.9463 - val_accuracy: 0.3990 - val_loss: 1.9885
Epoch 49/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3206 - loss: 1.9696 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3276 - loss: 1.9595
[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3311 - loss: 1.9559
[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3331 - loss: 1.9550
[1m128/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3348 - loss: 1.9534
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3358 - loss: 1.9522 - val_accuracy: 0.3887 - val_loss: 1.9998
Epoch 50/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3164 - loss: 2.0072 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3230 - loss: 1.9857
[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3267 - loss: 1.9771
[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3289 - loss: 1.9731
[1m126/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3308 - loss: 1.9697
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3323 - loss: 1.9665 - val_accuracy: 0.3861 - val_loss: 2.0314
Epoch 51/124

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[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3537 - loss: 1.9103 
[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3515 - loss: 1.9181
[1m 74/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3510 - loss: 1.9216
[1m 97/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3506 - loss: 1.9232
[1m119/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3502 - loss: 1.9240
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3497 - loss: 1.9247
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3497 - loss: 1.9247 - val_accuracy: 0.3885 - val_loss: 2.0169
Epoch 52/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3365 - loss: 1.9154 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3405 - loss: 1.9141
[1m 74/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3417 - loss: 1.9171
[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3415 - loss: 1.9192
[1m127/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3415 - loss: 1.9205
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3419 - loss: 1.9210 - val_accuracy: 0.3917 - val_loss: 2.0053
Epoch 53/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 25ms/step - accuracy: 0.2656 - loss: 1.9794
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3334 - loss: 1.9389 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3427 - loss: 1.9250
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3463 - loss: 1.9200
[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3483 - loss: 1.9177
[1m127/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3490 - loss: 1.9165
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3492 - loss: 1.9160 - val_accuracy: 0.4084 - val_loss: 1.9714
Epoch 54/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.3438 - loss: 2.0086
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3539 - loss: 1.9257 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3525 - loss: 1.9178
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3528 - loss: 1.9147
[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3534 - loss: 1.9129
[1m128/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3534 - loss: 1.9118
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3535 - loss: 1.9110 - val_accuracy: 0.3850 - val_loss: 2.0216
Epoch 55/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.2969 - loss: 2.0684
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3479 - loss: 1.9101 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3507 - loss: 1.9054
[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3521 - loss: 1.9022
[1m104/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3532 - loss: 1.9011
[1m131/145[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.3537 - loss: 1.9008
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3540 - loss: 1.9003 - val_accuracy: 0.3851 - val_loss: 2.0052
Epoch 56/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.3750 - loss: 1.9187
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3562 - loss: 1.8886 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3534 - loss: 1.8947
[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3552 - loss: 1.8956
[1m104/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3561 - loss: 1.8966
[1m131/145[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.3565 - loss: 1.8967
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3566 - loss: 1.8965 - val_accuracy: 0.3963 - val_loss: 1.9770
Epoch 57/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.3594 - loss: 1.8506
[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3511 - loss: 1.9241 
[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3540 - loss: 1.9216
[1m 75/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3552 - loss: 1.9171
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3560 - loss: 1.9134
[1m122/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3563 - loss: 1.9106
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3565 - loss: 1.9081 - val_accuracy: 0.3967 - val_loss: 1.9897
Epoch 58/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2656 - loss: 2.0245
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3379 - loss: 1.9315 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3463 - loss: 1.9152
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3505 - loss: 1.9060
[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3532 - loss: 1.9006
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3546 - loss: 1.8972
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3557 - loss: 1.8951 - val_accuracy: 0.3977 - val_loss: 1.9828

[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:14[0m 882ms/step
[1m57/86[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 894us/step  
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step 
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 11ms/step
Saved model to disk.

Accuracy capturado en la ejecución 28: 32.15 [%]
F1-score capturado en la ejecución 28: 31.41 [%]

=== EJECUCIÓN 29 ===

--- TRAIN (ejecución 29) ---

--- TEST (ejecución 29) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 409,231 (1.56 MB)
 Trainable params: 409,231 (1.56 MB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 6, 250)
(18469, 6, 250)

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9:56[0m 1s/step
[1m 56/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 920us/step
[1m112/578[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 909us/step
[1m173/578[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 879us/step
[1m239/578[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 850us/step
[1m297/578[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 854us/step
[1m352/578[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 865us/step
[1m411/578[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 863us/step
[1m475/578[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 853us/step
[1m538/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 847us/step
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 2ms/step

[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m57/86[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 894us/step
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step  
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 9ms/step

[1m  1/158[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 17ms/step
[1m 56/158[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 914us/step
[1m121/158[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 839us/step
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 39.77 [%]
Global F1 score (validation) = 37.59 [%]
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[[1.6524249e-03 5.7217188e-04 9.0828782e-04 ... 3.2344010e-02
  2.1197775e-03 3.8381742e-04]
 [1.9026656e-03 8.6255069e-04 1.0849491e-03 ... 5.8992531e-02
  2.5839258e-03 3.9506931e-04]
 [1.9706835e-03 4.6895683e-04 1.2564305e-03 ... 3.8290717e-02
  2.6966792e-03 9.2056341e-04]
 ...
 [1.4771806e-01 3.6128048e-02 2.3287520e-01 ... 2.5263961e-04
  2.3168866e-01 1.1206100e-01]
 [2.0954379e-01 1.1574839e-01 1.6695581e-01 ... 1.3995175e-03
  1.6772670e-01 7.2693348e-02]
 [1.8722321e-01 4.3139752e-02 1.9951878e-01 ... 3.8170003e-04
  2.5331235e-01 7.6918006e-02]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 44.17 [%]
Global accuracy score (test) = 33.98 [%]
Global F1 score (train) = 43.69 [%]
Global F1 score (test) = 33.29 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.31      0.36      0.33       184
 CAMINAR CON MÓVIL O LIBRO       0.36      0.31      0.33       184
       CAMINAR USUAL SPEED       0.21      0.10      0.14       184
            CAMINAR ZIGZAG       0.15      0.11      0.13       184
          DE PIE BARRIENDO       0.33      0.26      0.29       184
   DE PIE DOBLANDO TOALLAS       0.42      0.36      0.39       184
    DE PIE MOVIENDO LIBROS       0.38      0.26      0.31       184
          DE PIE USANDO PC       0.31      0.26      0.28       184
        FASE REPOSO CON K5       0.41      0.74      0.53       184
INCREMENTAL CICLOERGOMETRO       0.58      0.59      0.58       184
           SENTADO LEYENDO       0.38      0.39      0.39       184
         SENTADO USANDO PC       0.19      0.15      0.16       184
      SENTADO VIENDO LA TV       0.38      0.33      0.35       184
   SUBIR Y BAJAR ESCALERAS       0.17      0.34      0.23       184
                    TROTAR       0.56      0.56      0.56       161

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

2025-10-28 13:09:38.937295: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-28 13:09:38.948613: 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 00:00:1761653378.962068 1928813 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761653378.966408 1928813 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761653378.976675 1928813 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761653378.976697 1928813 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761653378.976699 1928813 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761653378.976700 1928813 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-28 13:09:38.979784: 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 00:00:1761653381.362311 1928813 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13749 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 409,231 (1.56 MB)
 Trainable params: 409,231 (1.56 MB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/124
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761653383.936771 1928935 service.cc:152] XLA service 0x700004011ea0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761653383.936811 1928935 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-28 13:09:43.989792: 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 00:00:1761653384.281743 1928935 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761653387.895457 1928935 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13:48[0m 6s/step - accuracy: 0.0703 - loss: 3.2873
[1m 24/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.0790 - loss: 3.2633  
[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.0791 - loss: 3.2301
[1m 73/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.0796 - loss: 3.2077
[1m 97/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.0803 - loss: 3.1903
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
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Epoch 2/124

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Epoch 3/124

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Epoch 4/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1344 - loss: 2.7835 
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[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1351 - loss: 2.7826
[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1350 - loss: 2.7822
[1m126/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1348 - loss: 2.7818
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.1346 - loss: 2.7815 - val_accuracy: 0.2230 - val_loss: 2.3577
Epoch 5/124

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[1m 24/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1280 - loss: 2.7530 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1315 - loss: 2.7426
[1m 74/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1331 - loss: 2.7359
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1335 - loss: 2.7336
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1340 - loss: 2.7313
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.1347 - loss: 2.7289 - val_accuracy: 0.2313 - val_loss: 2.3399
Epoch 6/124

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[1m 24/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1473 - loss: 2.6679 
[1m 48/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1475 - loss: 2.6774
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[1m 96/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1481 - loss: 2.6793
[1m122/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1487 - loss: 2.6780
[1m143/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.1490 - loss: 2.6766
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.1490 - loss: 2.6764 - val_accuracy: 0.2511 - val_loss: 2.3191
Epoch 7/124

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[1m 23/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1705 - loss: 2.6104 
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[1m 96/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1645 - loss: 2.6208
[1m119/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1645 - loss: 2.6189
[1m143/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.1645 - loss: 2.6173
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Epoch 8/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1664 - loss: 2.5936 
[1m 53/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1649 - loss: 2.5925
[1m 80/145[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1649 - loss: 2.5893
[1m105/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1655 - loss: 2.5857
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[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.1669 - loss: 2.5810 - val_accuracy: 0.2877 - val_loss: 2.2582
Epoch 9/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1659 - loss: 2.5519 
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[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1738 - loss: 2.5449
[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1752 - loss: 2.5436
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[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.1764 - loss: 2.5408 - val_accuracy: 0.2845 - val_loss: 2.2580
Epoch 10/124

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[1m 24/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1774 - loss: 2.5122 
[1m 48/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1805 - loss: 2.5086
[1m 72/145[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1811 - loss: 2.5069
[1m 96/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1816 - loss: 2.5058
[1m121/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1824 - loss: 2.5044
[1m141/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.1827 - loss: 2.5034
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.1827 - loss: 2.5033 - val_accuracy: 0.2954 - val_loss: 2.2267
Epoch 11/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.1641 - loss: 2.5760
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1886 - loss: 2.4696 
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[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1929 - loss: 2.4625
[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1936 - loss: 2.4621
[1m129/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1939 - loss: 2.4617
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.1942 - loss: 2.4616 - val_accuracy: 0.3085 - val_loss: 2.2137
Epoch 12/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.1719 - loss: 2.5451
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1899 - loss: 2.4915 
[1m 53/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1910 - loss: 2.4735
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1930 - loss: 2.4644
[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1944 - loss: 2.4589
[1m127/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1952 - loss: 2.4553
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.1958 - loss: 2.4530 - val_accuracy: 0.3149 - val_loss: 2.2119
Epoch 13/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2422 - loss: 2.3841
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2117 - loss: 2.4041 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2096 - loss: 2.4064
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2084 - loss: 2.4052
[1m 97/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2083 - loss: 2.4058
[1m123/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2083 - loss: 2.4063
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2082 - loss: 2.4065 - val_accuracy: 0.3260 - val_loss: 2.1931
Epoch 14/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2188 - loss: 2.3013
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2167 - loss: 2.3588 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2154 - loss: 2.3727
[1m 75/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2145 - loss: 2.3809
[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2150 - loss: 2.3827
[1m118/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2152 - loss: 2.3837
[1m143/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.2151 - loss: 2.3844
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Epoch 15/124

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Epoch 16/124

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Epoch 17/124

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[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2372 - loss: 2.3301 
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[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2342 - loss: 2.3284
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2340 - loss: 2.3277
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2340 - loss: 2.3271 - val_accuracy: 0.3405 - val_loss: 2.1694
Epoch 18/124

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[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2341 - loss: 2.3238 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2367 - loss: 2.3118
[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2364 - loss: 2.3087
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2363 - loss: 2.3082
[1m126/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2365 - loss: 2.3069
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2368 - loss: 2.3059 - val_accuracy: 0.3413 - val_loss: 2.1482
Epoch 19/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.2734 - loss: 2.2904
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2428 - loss: 2.3126 
[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2394 - loss: 2.3162
[1m 75/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2393 - loss: 2.3112
[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2392 - loss: 2.3084
[1m121/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2390 - loss: 2.3058
[1m144/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.2392 - loss: 2.3034
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2392 - loss: 2.3032 - val_accuracy: 0.3556 - val_loss: 2.1431
Epoch 20/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2400 - loss: 2.3098 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2434 - loss: 2.2939
[1m 79/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2435 - loss: 2.2906
[1m105/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2440 - loss: 2.2877
[1m132/145[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.2444 - loss: 2.2854
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2447 - loss: 2.2846 - val_accuracy: 0.3633 - val_loss: 2.1386
Epoch 21/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2661 - loss: 2.2362 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2633 - loss: 2.2442
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2606 - loss: 2.2465
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2595 - loss: 2.2475
[1m123/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2588 - loss: 2.2480
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2584 - loss: 2.2477 - val_accuracy: 0.3669 - val_loss: 2.1337
Epoch 22/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2502 - loss: 2.2712 
[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2510 - loss: 2.2682
[1m 72/145[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2535 - loss: 2.2627
[1m 94/145[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2553 - loss: 2.2586
[1m119/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2565 - loss: 2.2551
[1m142/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.2572 - loss: 2.2524
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2574 - loss: 2.2519 - val_accuracy: 0.3661 - val_loss: 2.1322
Epoch 23/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2188 - loss: 2.3406
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2472 - loss: 2.2602 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2474 - loss: 2.2598
[1m 75/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2478 - loss: 2.2578
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2488 - loss: 2.2537
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2499 - loss: 2.2502
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2509 - loss: 2.2473 - val_accuracy: 0.3562 - val_loss: 2.1137
Epoch 24/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.3203 - loss: 2.1953
[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2540 - loss: 2.2314 
[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2568 - loss: 2.2229
[1m 73/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2602 - loss: 2.2180
[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2617 - loss: 2.2164
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2625 - loss: 2.2153
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2629 - loss: 2.2150 - val_accuracy: 0.3742 - val_loss: 2.1137
Epoch 25/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 25ms/step - accuracy: 0.2422 - loss: 2.2374
[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2673 - loss: 2.2086 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2649 - loss: 2.2132
[1m 77/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2660 - loss: 2.2124
[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2672 - loss: 2.2110
[1m127/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2678 - loss: 2.2105
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2682 - loss: 2.2097 - val_accuracy: 0.3585 - val_loss: 2.1220
Epoch 26/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2734 - loss: 2.1287
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2784 - loss: 2.1750 
[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2748 - loss: 2.1815
[1m 73/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2724 - loss: 2.1844
[1m 97/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2710 - loss: 2.1859
[1m122/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2701 - loss: 2.1869
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2703 - loss: 2.1863 - val_accuracy: 0.3687 - val_loss: 2.0986
Epoch 27/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.2422 - loss: 2.2727
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2568 - loss: 2.2107 
[1m 48/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2638 - loss: 2.1943
[1m 74/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2680 - loss: 2.1866
[1m 95/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2701 - loss: 2.1831
[1m118/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2717 - loss: 2.1806
[1m142/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.2730 - loss: 2.1787
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2732 - loss: 2.1785 - val_accuracy: 0.3730 - val_loss: 2.0852
Epoch 28/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.2266 - loss: 2.2769
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2871 - loss: 2.1572 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2862 - loss: 2.1544
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2851 - loss: 2.1561
[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2847 - loss: 2.1563
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2840 - loss: 2.1569
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Epoch 29/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2876 - loss: 2.1659 
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[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2828 - loss: 2.1527
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Epoch 30/124

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[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2847 - loss: 2.1215 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2877 - loss: 2.1136
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[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2885 - loss: 2.1141
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Epoch 31/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3042 - loss: 2.1219 
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[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2989 - loss: 2.1217
[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2973 - loss: 2.1226
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2963 - loss: 2.1232
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2958 - loss: 2.1229 - val_accuracy: 0.3756 - val_loss: 2.0800
Epoch 32/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2812 - loss: 2.0875
[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2807 - loss: 2.1294 
[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2844 - loss: 2.1251
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[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2871 - loss: 2.1206
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2879 - loss: 2.1191
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2889 - loss: 2.1176 - val_accuracy: 0.3836 - val_loss: 2.0486
Epoch 33/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2500 - loss: 2.2529
[1m 24/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2814 - loss: 2.1286 
[1m 48/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2880 - loss: 2.1133
[1m 73/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2916 - loss: 2.1042
[1m 95/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2926 - loss: 2.1021
[1m120/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2935 - loss: 2.1011
[1m143/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.2948 - loss: 2.0994
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2949 - loss: 2.0993 - val_accuracy: 0.3705 - val_loss: 2.0575
Epoch 34/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.3125 - loss: 2.0745
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2943 - loss: 2.1023 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2974 - loss: 2.0917
[1m 73/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2982 - loss: 2.0893
[1m 96/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2991 - loss: 2.0875
[1m120/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2997 - loss: 2.0866
[1m144/145[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.2998 - loss: 2.0858
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.2998 - loss: 2.0858 - val_accuracy: 0.3905 - val_loss: 2.0386
Epoch 35/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.3516 - loss: 2.0778
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3096 - loss: 2.0738 
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Epoch 36/124

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Epoch 37/124

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[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3066 - loss: 2.0618
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Epoch 38/124

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[1m 24/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2950 - loss: 2.0831 
[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2997 - loss: 2.0767
[1m 71/145[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3020 - loss: 2.0727
[1m 97/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3045 - loss: 2.0678
[1m123/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3060 - loss: 2.0639
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Epoch 39/124

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[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3358 - loss: 1.9827 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3276 - loss: 2.0046
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3233 - loss: 2.0154
[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3202 - loss: 2.0205
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3183 - loss: 2.0242
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3174 - loss: 2.0262 - val_accuracy: 0.3988 - val_loss: 2.0028
Epoch 40/124

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[1m 21/145[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step - accuracy: 0.3123 - loss: 2.0888 
[1m 47/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3169 - loss: 2.0664
[1m 74/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3178 - loss: 2.0533
[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3183 - loss: 2.0456
[1m123/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3185 - loss: 2.0401
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3186 - loss: 2.0369 - val_accuracy: 0.3820 - val_loss: 2.0238
Epoch 41/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3152 - loss: 2.0351 
[1m 51/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3179 - loss: 2.0299
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[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3192 - loss: 2.0252
[1m125/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3194 - loss: 2.0245
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3193 - loss: 2.0242 - val_accuracy: 0.3973 - val_loss: 2.0200
Epoch 42/124

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Epoch 43/124

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Epoch 44/124

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Epoch 45/124

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[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3235 - loss: 1.9894
[1m 74/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3248 - loss: 1.9898
[1m 98/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3263 - loss: 1.9874
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3273 - loss: 1.9858
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Epoch 46/124

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[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3376 - loss: 1.9386 
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[1m100/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3384 - loss: 1.9458
[1m126/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3380 - loss: 1.9482
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3377 - loss: 1.9501 - val_accuracy: 0.3996 - val_loss: 1.9968
Epoch 47/124

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[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3346 - loss: 1.9919 
[1m 49/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3331 - loss: 1.9880
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[1m 99/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3351 - loss: 1.9804
[1m122/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3355 - loss: 1.9776
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3358 - loss: 1.9755
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3358 - loss: 1.9754 - val_accuracy: 0.4080 - val_loss: 2.0048
Epoch 48/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3518 - loss: 1.9260 
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[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3467 - loss: 1.9416
[1m103/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3455 - loss: 1.9453
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Epoch 49/124

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[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3228 - loss: 1.9944 
[1m 53/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3275 - loss: 1.9703
[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3304 - loss: 1.9609
[1m105/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3326 - loss: 1.9564
[1m131/145[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.3339 - loss: 1.9539
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3345 - loss: 1.9531 - val_accuracy: 0.4116 - val_loss: 1.9708
Epoch 50/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.3672 - loss: 1.8275
[1m 26/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3462 - loss: 1.8836 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3479 - loss: 1.8959
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3476 - loss: 1.9004
[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3471 - loss: 1.9055
[1m124/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3468 - loss: 1.9088
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3463 - loss: 1.9119 - val_accuracy: 0.4086 - val_loss: 2.0016
Epoch 51/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.3516 - loss: 1.8033
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3567 - loss: 1.9050 
[1m 52/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3560 - loss: 1.9058
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3543 - loss: 1.9089
[1m102/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3521 - loss: 1.9129
[1m129/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3512 - loss: 1.9149
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3508 - loss: 1.9159 - val_accuracy: 0.4127 - val_loss: 1.9811
Epoch 52/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.4531 - loss: 1.7113
[1m 27/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3598 - loss: 1.8977 
[1m 53/145[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3516 - loss: 1.9120
[1m 78/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3506 - loss: 1.9145
[1m104/145[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3503 - loss: 1.9160
[1m128/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3500 - loss: 1.9173
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3500 - loss: 1.9177 - val_accuracy: 0.4213 - val_loss: 2.0029
Epoch 53/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2344 - loss: 2.0608
[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3242 - loss: 1.9368 
[1m 48/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3296 - loss: 1.9299
[1m 72/145[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3345 - loss: 1.9277
[1m 96/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3380 - loss: 1.9251
[1m121/145[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3403 - loss: 1.9225
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3418 - loss: 1.9210 - val_accuracy: 0.4056 - val_loss: 1.9855
Epoch 54/124

[1m  1/145[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.3516 - loss: 1.9153
[1m 25/145[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3512 - loss: 1.9106 
[1m 50/145[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3524 - loss: 1.9046
[1m 76/145[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3536 - loss: 1.9018
[1m101/145[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3540 - loss: 1.9015
[1m126/145[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3539 - loss: 1.9018
[1m145/145[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.3537 - loss: 1.9021 - val_accuracy: 0.4133 - val_loss: 1.9845

[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:13[0m 867ms/step
[1m59/86[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 863us/step  
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step 
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 11ms/step
Saved model to disk.

Accuracy capturado en la ejecución 29: 33.98 [%]
F1-score capturado en la ejecución 29: 33.29 [%]

=== EJECUCIÓN 30 ===

--- TRAIN (ejecución 30) ---

--- TEST (ejecución 30) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 6, 128)         │       160,128 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_2 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_2           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_3 (Conv1D)               │ (None, 6, 128)         │        82,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_3           │ (None, 6, 128)         │           256 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_3 (Dropout)             │ (None, 6, 128)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 128)            │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_4 (Dropout)             │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 15)             │         1,935 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 409,231 (1.56 MB)
 Trainable params: 409,231 (1.56 MB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(2737, 6, 250)
(18469, 6, 250)

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[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 17ms/step
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[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 9ms/step

[1m  1/158[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step
[1m 57/158[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 909us/step
[1m117/158[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 880us/step
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
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Global accuracy score (validation) = 41.33 [%]
Global F1 score (validation) = 39.07 [%]
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[[0.00130921 0.00037643 0.00109633 ... 0.020748   0.00214422 0.00040707]
 [0.00120957 0.00067392 0.00143446 ... 0.0757533  0.00245247 0.0003887 ]
 [0.01450114 0.00395052 0.01240922 ... 0.07787388 0.01866701 0.00447636]
 ...
 [0.20637256 0.05044007 0.1950065  ... 0.00063692 0.23192742 0.07106823]
 [0.2286294  0.10025745 0.1732047  ... 0.00169799 0.18020023 0.06011085]
 [0.20567834 0.04888813 0.18677886 ... 0.00066714 0.24543934 0.06907277]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 43.31 [%]
Global accuracy score (test) = 32.48 [%]
Global F1 score (train) = 42.64 [%]
Global F1 score (test) = 31.34 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.31      0.40      0.35       184
 CAMINAR CON MÓVIL O LIBRO       0.40      0.29      0.34       184
       CAMINAR USUAL SPEED       0.10      0.03      0.04       184
            CAMINAR ZIGZAG       0.09      0.06      0.07       184
          DE PIE BARRIENDO       0.32      0.26      0.29       184
   DE PIE DOBLANDO TOALLAS       0.30      0.35      0.32       184
    DE PIE MOVIENDO LIBROS       0.32      0.18      0.23       184
          DE PIE USANDO PC       0.22      0.23      0.23       184
        FASE REPOSO CON K5       0.43      0.74      0.55       184
INCREMENTAL CICLOERGOMETRO       0.49      0.63      0.55       184
           SENTADO LEYENDO       0.44      0.30      0.35       184
         SENTADO USANDO PC       0.19      0.14      0.16       184
      SENTADO VIENDO LA TV       0.41      0.33      0.36       184
   SUBIR Y BAJAR ESCALERAS       0.17      0.39      0.23       184
                    TROTAR       0.68      0.57      0.62       161

                  accuracy                           0.32      2737
                 macro avg       0.32      0.33      0.31      2737
              weighted avg       0.32      0.32      0.31      2737


Accuracy capturado en la ejecución 30: 32.48 [%]
F1-score capturado en la ejecución 30: 31.34 [%]

=== RESUMEN FINAL ===
Accuracies: [32.88, 31.31, 32.74, 35.48, 31.75, 33.5, 32.33, 33.65, 33.69, 32.59, 34.97, 32.15, 33.8, 32.01, 32.08, 35.33, 33.65, 34.34, 35.26, 35.22, 31.6, 35.26, 32.52, 33.07, 33.14, 34.86, 33.91, 32.15, 33.98, 32.48]
F1-scores: [32.16, 31.48, 32.52, 34.67, 31.19, 32.38, 31.47, 32.86, 32.97, 32.43, 34.43, 31.61, 33.17, 31.3, 30.8, 34.66, 33.31, 33.35, 34.29, 34.82, 30.85, 34.39, 31.88, 31.85, 32.66, 34.06, 33.15, 31.41, 33.29, 31.34]
Accuracy mean: 33.3900 | std: 1.2406
F1 mean: 32.6917 | std: 1.2244

Resultados guardados en /mnt/nvme1n2/git/uniovi-simur-wearablepermed-data/output/Paper_results/cases_dataset_M/case_M_ESANN_acc_gyr_17_classes/metrics_test.npz
