2025-11-07 16:36:54.111195: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 16:36:54.122920: 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 00:00:1762529814.137140 3215107 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762529814.141566 3215107 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762529814.152123 3215107 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762529814.152143 3215107 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762529814.152145 3215107 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762529814.152147 3215107 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 16:36:54.155478: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/ray/tune/impl/tuner_internal.py:144: RayDeprecationWarning: The `RunConfig` class should be imported from `ray.tune` when passing it to the Tuner. Please update your imports. See this issue for more context and migration options: https://github.com/ray-project/ray/issues/49454. Disable these warnings by setting the environment variable: RAY_TRAIN_ENABLE_V2_MIGRATION_WARNINGS=0
  _log_deprecation_warning(
2025-11-07 16:36:57,129	INFO worker.py:1927 -- Started a local Ray instance.
2025-11-07 16:36:57,844	INFO tune.py:253 -- Initializing Ray automatically. For cluster usage or custom Ray initialization, call `ray.init(...)` before `Tuner(...)`.
2025-11-07 16:36:57,919	INFO trial.py:182 -- Creating a new dirname dir_987ea_a101 because trial dirname 'dir_987ea' already exists.
2025-11-07 16:36:57,922	INFO trial.py:182 -- Creating a new dirname dir_987ea_01fd because trial dirname 'dir_987ea' already exists.
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2025-11-07 16:36:57,927	INFO trial.py:182 -- Creating a new dirname dir_987ea_593e because trial dirname 'dir_987ea' already exists.
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2025-11-07 16:36:57,934	INFO trial.py:182 -- Creating a new dirname dir_987ea_2138 because trial dirname 'dir_987ea' already exists.
2025-11-07 16:36:57,936	INFO trial.py:182 -- Creating a new dirname dir_987ea_2ca0 because trial dirname 'dir_987ea' already exists.
2025-11-07 16:36:57,938	INFO trial.py:182 -- Creating a new dirname dir_987ea_f27c because trial dirname 'dir_987ea' already exists.
2025-11-07 16:36:57,941	INFO trial.py:182 -- Creating a new dirname dir_987ea_0000 because trial dirname 'dir_987ea' already exists.
2025-11-07 16:36:57,943	INFO trial.py:182 -- Creating a new dirname dir_987ea_176f because trial dirname 'dir_987ea' already exists.
2025-11-07 16:36:57,946	INFO trial.py:182 -- Creating a new dirname dir_987ea_f0ef because trial dirname 'dir_987ea' already exists.
2025-11-07 16:36:57,949	INFO trial.py:182 -- Creating a new dirname dir_987ea_76c7 because trial dirname 'dir_987ea' already exists.
2025-11-07 16:36:57,953	INFO trial.py:182 -- Creating a new dirname dir_987ea_e0c2 because trial dirname 'dir_987ea' already exists.
2025-11-07 16:36:57,956	INFO trial.py:182 -- Creating a new dirname dir_987ea_e369 because trial dirname 'dir_987ea' already exists.
2025-11-07 16:36:57,959	INFO trial.py:182 -- Creating a new dirname dir_987ea_e763 because trial dirname 'dir_987ea' already exists.
2025-11-07 16:36:57,963	INFO trial.py:182 -- Creating a new dirname dir_987ea_8db6 because trial dirname 'dir_987ea' already exists.
2025-11-07 16:36:57,967	INFO trial.py:182 -- Creating a new dirname dir_987ea_cfb8 because trial dirname 'dir_987ea' already exists.
2025-11-07 16:36:57,974	INFO trial.py:182 -- Creating a new dirname dir_987ea_e32e because trial dirname 'dir_987ea' already exists.
1 GPU(s) detected and VRAM set to crossover mode..
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Se lanza la búsqueda de hiperparámetros óptimos del modelo
╭─────────────────────────────────────────────────────────────────────╮
│ Configuration for experiment     CAPTURE24_hyperparameters_tuning   │
├─────────────────────────────────────────────────────────────────────┤
│ Search algorithm                 BasicVariantGenerator              │
│ Scheduler                        FIFOScheduler                      │
│ Number of trials                 20                                 │
╰─────────────────────────────────────────────────────────────────────╯

View detailed results here: /mnt/nvme1n2/git/uniovi-simur-wearablepermed-data/output/Paper_results/cases_dataset_M/case_M_CAPTURE24_acc_superclasses_CPA_METs/CAPTURE24_hyperparameters_tuning
To visualize your results with TensorBoard, run: `tensorboard --logdir /tmp/ray/session_2025-11-07_16-36-56_394989_3215107/artifacts/2025-11-07_16-36-57/CAPTURE24_hyperparameters_tuning/driver_artifacts`

Trial status: 20 PENDING
Current time: 2025-11-07 16:36:58. Total running time: 0s
Logical resource usage: 0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status       N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     num_resblocks     tasa_aprendizaje     epochs │
├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_987ea    PENDING            2   adam            tanh                                   16                 32                  3                 0          1.76369e-05         22 │
│ trial_987ea    PENDING            3   adam            tanh                                   16                 64                  3                 1          0.000124674         27 │
│ trial_987ea    PENDING            3   rmsprop         tanh                                   32                 16                  3                 1          0.0001207           28 │
│ trial_987ea    PENDING            2   rmsprop         tanh                                   16                 64                  3                 1          8.8649e-06          23 │
│ trial_987ea    PENDING            2   adam            tanh                                   32                 16                  3                 1          0.000103558         26 │
│ trial_987ea    PENDING            2   rmsprop         tanh                                   32                 16                  5                 0          0.000131282         20 │
│ trial_987ea    PENDING            2   rmsprop         relu                                   32                 32                  5                 0          4.30906e-05         29 │
│ trial_987ea    PENDING            3   rmsprop         relu                                   32                 32                  3                 0          6.42207e-06         25 │
│ trial_987ea    PENDING            3   rmsprop         relu                                   16                 64                  5                 1          6.20968e-06         17 │
│ trial_987ea    PENDING            2   rmsprop         tanh                                   32                 16                  3                 0          2.39155e-05         26 │
│ trial_987ea    PENDING            3   rmsprop         tanh                                   32                 32                  3                 0          2.14512e-05         19 │
│ trial_987ea    PENDING            3   adam            relu                                   16                 64                  5                 0          3.57777e-05         28 │
│ trial_987ea    PENDING            2   adam            relu                                   32                 16                  3                 0          4.86659e-05         23 │
│ trial_987ea    PENDING            3   rmsprop         tanh                                   16                 64                  3                 1          4.71571e-05         22 │
│ trial_987ea    PENDING            3   adam            relu                                   16                 64                  5                 0          9.94182e-05         27 │
│ trial_987ea    PENDING            2   adam            tanh                                   16                 16                  3                 0          0.000100097         16 │
│ trial_987ea    PENDING            2   adam            relu                                   32                 64                  3                 1          1.89531e-05         28 │
│ trial_987ea    PENDING            3   adam            tanh                                   32                 64                  5                 1          0.000197693         21 │
│ trial_987ea    PENDING            3   rmsprop         relu                                   16                 64                  5                 1          0.000102887         19 │
│ trial_987ea    PENDING            2   rmsprop         relu                                   16                 16                  3                 0          4.74263e-05         18 │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

Trial trial_987ea started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_987ea config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                            22 │
│ funcion_activacion              tanh │
│ num_resblocks                      0 │
│ numero_filtros                    32 │
│ optimizador                     adam │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  16 │
│ tasa_aprendizaje             0.00002 │
╰──────────────────────────────────────╯
Trial trial_987ea started with configuration:
╭─────────────────────────────────────╮
│ Trial trial_987ea config            │
├─────────────────────────────────────┤
│ N_capas                           3 │
│ epochs                           27 │
│ funcion_activacion             relu │
│ num_resblocks                     0 │
│ numero_filtros                   64 │
│ optimizador                    adam │
│ tamanho_filtro                    5 │
│ tamanho_minilote                 16 │
│ tasa_aprendizaje             0.0001 │
╰─────────────────────────────────────╯
Trial trial_987ea started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_987ea config             │
├──────────────────────────────────────┤
│ N_capas                            3 │
│ epochs                            19 │
│ funcion_activacion              relu │
│ num_resblocks                      1 │
│ numero_filtros                    64 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  16 │
│ tasa_aprendizaje              0.0001 │
╰──────────────────────────────────────╯
Trial trial_987ea started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_987ea config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                            26 │
│ funcion_activacion              tanh │
│ num_resblocks                      0 │
│ numero_filtros                    16 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00002 │
╰──────────────────────────────────────╯
Trial trial_987ea started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_987ea config             │
├──────────────────────────────────────┤
│ N_capas                            3 │
│ epochs                            17 │
│ funcion_activacion              relu │
│ num_resblocks                      1 │
│ numero_filtros                    64 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  16 │
│ tasa_aprendizaje             0.00001 │
╰──────────────────────────────────────╯
Trial trial_987ea started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_987ea config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                            29 │
│ funcion_activacion              relu │
│ num_resblocks                      0 │
│ numero_filtros                    32 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00004 │
╰──────────────────────────────────────╯
Trial trial_987ea started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_987ea config             │
├──────────────────────────────────────┤
│ N_capas                            3 │
│ epochs                            28 │
│ funcion_activacion              tanh │
│ num_resblocks                      1 │
│ numero_filtros                    16 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00012 │
╰──────────────────────────────────────╯
Trial trial_987ea started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_987ea config             │
├──────────────────────────────────────┤
│ N_capas                            3 │
│ epochs                            25 │
│ funcion_activacion              relu │
│ num_resblocks                      0 │
│ numero_filtros                    32 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00001 │
╰──────────────────────────────────────╯
Trial trial_987ea started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_987ea config             │
├──────────────────────────────────────┤
│ N_capas                            3 │
│ epochs                            27 │
│ funcion_activacion              tanh │
│ num_resblocks                      1 │
│ numero_filtros                    64 │
│ optimizador                     adam │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  16 │
│ tasa_aprendizaje             0.00012 │
╰──────────────────────────────────────╯
Trial trial_987ea started with configuration:
╭─────────────────────────────────────╮
│ Trial trial_987ea config            │
├─────────────────────────────────────┤
│ N_capas                           3 │
│ epochs                           21 │
│ funcion_activacion             tanh │
│ num_resblocks                     1 │
│ numero_filtros                   64 │
│ optimizador                    adam │
│ tamanho_filtro                    5 │
│ tamanho_minilote                 32 │
│ tasa_aprendizaje             0.0002 │
╰─────────────────────────────────────╯
Trial trial_987ea started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_987ea config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                            23 │
│ funcion_activacion              tanh │
│ num_resblocks                      1 │
│ numero_filtros                    64 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  16 │
│ tasa_aprendizaje             0.00001 │
╰──────────────────────────────────────╯
Trial trial_987ea started with configuration:
[36m(train_cnn_ray_tune pid=3216723)[0m 2025-11-07 16:37:01.140224: 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=3216757)[0m 2025-11-07 16:37:01.266269: 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=3216723)[0m WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
[36m(train_cnn_ray_tune pid=3216723)[0m E0000 00:00:1762529821.191976 3217886 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=3216723)[0m E0000 00:00:1762529821.199752 3217886 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=3216723)[0m W0000 00:00:1762529821.219484 3217886 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=3216723)[0m W0000 00:00:1762529821.219541 3217886 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=3216723)[0m W0000 00:00:1762529821.219543 3217886 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=3216723)[0m W0000 00:00:1762529821.219546 3217886 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=3216723)[0m 2025-11-07 16:37:01.225440: 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=3216723)[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=3216757)[0m 2025-11-07 16:37:04.458297: 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=3216757)[0m 2025-11-07 16:37:04.458349: I external/local_xla/xla/stream_executor/cuda/cuda_diagnostics.cc:167] env: CUDA_VISIBLE_DEVICES=""
[36m(train_cnn_ray_tune pid=3216757)[0m 2025-11-07 16:37:04.458357: 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=3216757)[0m 2025-11-07 16:37:04.458362: 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=3216757)[0m 2025-11-07 16:37:04.458367: 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=3216757)[0m 2025-11-07 16:37:04.458372: I external/local_xla/xla/stream_executor/cuda/cuda_diagnostics.cc:190] hostname: simur-MS-7B94
[36m(train_cnn_ray_tune pid=3216757)[0m 2025-11-07 16:37:04.458617: 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=3216757)[0m 2025-11-07 16:37:04.458646: 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=3216757)[0m 2025-11-07 16:37:04.458650: I external/local_xla/xla/stream_executor/cuda/cuda_diagnostics.cc:291] kernel version seems to match DSO: 570.133.7
╭──────────────────────────────────────╮
│ Trial trial_987ea config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                            18 │
│ funcion_activacion              relu │
│ num_resblocks                      0 │
│ numero_filtros                    16 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  16 │
│ tasa_aprendizaje             0.00005 │
╰──────────────────────────────────────╯
Trial trial_987ea started with configuration:
╭─────────────────────────────────────╮
│ Trial trial_987ea config            │
├─────────────────────────────────────┤
│ N_capas                           2 │
│ epochs                           16 │
│ funcion_activacion             tanh │
│ num_resblocks                     0 │
│ numero_filtros                   16 │
│ optimizador                    adam │
│ tamanho_filtro                    3 │
│ tamanho_minilote                 16 │
│ tasa_aprendizaje             0.0001 │
╰─────────────────────────────────────╯
Trial trial_987ea started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_987ea config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                            23 │
│ funcion_activacion              relu │
│ num_resblocks                      0 │
│ numero_filtros                    16 │
│ optimizador                     adam │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00005 │
╰──────────────────────────────────────╯
Trial trial_987ea started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_987ea config             │
├──────────────────────────────────────┤
│ N_capas                            3 │
│ epochs                            19 │
│ funcion_activacion              tanh │
│ num_resblocks                      0 │
│ numero_filtros                    32 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00002 │
╰──────────────────────────────────────╯
Trial trial_987ea started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_987ea config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                            28 │
│ funcion_activacion              relu │
│ num_resblocks                      1 │
│ numero_filtros                    64 │
│ optimizador                     adam │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00002 │
╰──────────────────────────────────────╯
Trial trial_987ea started with configuration:
╭─────────────────────────────────────╮
│ Trial trial_987ea config            │
├─────────────────────────────────────┤
│ N_capas                           2 │
│ epochs                           26 │
│ funcion_activacion             tanh │
│ num_resblocks                     1 │
│ numero_filtros                   16 │
│ optimizador                    adam │
│ tamanho_filtro                    3 │
│ tamanho_minilote                 32 │
│ tasa_aprendizaje             0.0001 │
╰─────────────────────────────────────╯
Trial trial_987ea started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_987ea config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                            20 │
│ funcion_activacion              tanh │
│ num_resblocks                      0 │
│ numero_filtros                    16 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00013 │
╰──────────────────────────────────────╯
Trial trial_987ea started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_987ea config             │
├──────────────────────────────────────┤
│ N_capas                            3 │
│ epochs                            22 │
│ funcion_activacion              tanh │
│ num_resblocks                      1 │
│ numero_filtros                    64 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  16 │
│ tasa_aprendizaje             0.00005 │
╰──────────────────────────────────────╯
Trial trial_987ea started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_987ea config             │
├──────────────────────────────────────┤
│ N_capas                            3 │
│ epochs                            28 │
│ funcion_activacion              relu │
│ num_resblocks                      0 │
│ numero_filtros                    64 │
│ optimizador                     adam │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  16 │
│ tasa_aprendizaje             0.00004 │
╰──────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3216723)[0m Epoch 1/22
[36m(train_cnn_ray_tune pid=3216754)[0m 
[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m16:04[0m 3s/step - accuracy: 0.1875 - loss: 1.9302
[36m(train_cnn_ray_tune pid=3216754)[0m 
[1m  4/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 17ms/step - accuracy: 0.2090 - loss: 2.0257 
[1m  7/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 19ms/step - accuracy: 0.2010 - loss: 2.1024
[36m(train_cnn_ray_tune pid=3216754)[0m 
[1m 11/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 18ms/step - accuracy: 0.1967 - loss: 2.1186
[1m 14/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 18ms/step - accuracy: 0.1944 - loss: 2.1256
[36m(train_cnn_ray_tune pid=3216754)[0m 
[1m 17/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 18ms/step - accuracy: 0.1927 - loss: 2.1317
[36m(train_cnn_ray_tune pid=3216754)[0m 
[1m 20/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 19ms/step - accuracy: 0.1920 - loss: 2.1337
[1m 23/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 19ms/step - accuracy: 0.1914 - loss: 2.1339
[36m(train_cnn_ray_tune pid=3216754)[0m 
[1m 26/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 18ms/step - accuracy: 0.1915 - loss: 2.1339
[1m 30/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 18ms/step - accuracy: 0.1916 - loss: 2.1343
[36m(train_cnn_ray_tune pid=3216754)[0m 
[1m 32/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 19ms/step - accuracy: 0.1915 - loss: 2.1334
[1m 35/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 19ms/step - accuracy: 0.1917 - loss: 2.1318
[36m(train_cnn_ray_tune pid=3216745)[0m 
[1m  4/655[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m15s[0m 23ms/step - accuracy: 0.1784 - loss: 2.3323
[1m  7/655[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 21ms/step - accuracy: 0.1719 - loss: 2.2874
[36m(train_cnn_ray_tune pid=3216754)[0m 
[1m 38/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 19ms/step - accuracy: 0.1920 - loss: 2.1292
[1m 41/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 19ms/step - accuracy: 0.1926 - loss: 2.1270
[36m(train_cnn_ray_tune pid=3216745)[0m 
[1m 11/655[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 19ms/step - accuracy: 0.1867 - loss: 2.2128
[36m(train_cnn_ray_tune pid=3216754)[0m 
[1m 44/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 19ms/step - accuracy: 0.1930 - loss: 2.1245
[1m 47/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 19ms/step - accuracy: 0.1934 - loss: 2.1226
[36m(train_cnn_ray_tune pid=3216729)[0m 
[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m17:27[0m 3s/step - accuracy: 0.2188 - loss: 2.0917
[1m  3/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 26ms/step - accuracy: 0.2205 - loss: 1.9975 
[36m(train_cnn_ray_tune pid=3216745)[0m 
[1m 14/655[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 18ms/step - accuracy: 0.1945 - loss: 2.1841
[1m 17/655[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 18ms/step - accuracy: 0.1977 - loss: 2.1679
[36m(train_cnn_ray_tune pid=3216754)[0m 
[1m 50/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m5s[0m 19ms/step - accuracy: 0.1938 - loss: 2.1210
[36m(train_cnn_ray_tune pid=3216748)[0m Epoch 1/20[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=3216754)[0m 
[1m123/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m4s[0m 22ms/step - accuracy: 0.2055 - loss: 2.0756
[1m124/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m4s[0m 23ms/step - accuracy: 0.2057 - loss: 2.0751
[1m126/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m4s[0m 23ms/step - accuracy: 0.2059 - loss: 2.0740
[36m(train_cnn_ray_tune pid=3216740)[0m 
[1m  2/655[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:01[0m 93ms/step - accuracy: 0.1719 - loss: 2.2560 
[36m(train_cnn_ray_tune pid=3216740)[0m 
[1m  3/655[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m53s[0m 82ms/step - accuracy: 0.1771 - loss: 2.1844 
[36m(train_cnn_ray_tune pid=3216740)[0m 
[1m  1/655[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:15:12[0m 7s/step - accuracy: 0.1875 - loss: 2.3283[32m [repeated 12x across cluster][0m
[36m(train_cnn_ray_tune pid=3216748)[0m 
[1m  4/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 18ms/step - accuracy: 0.2988 - loss: 1.8851 
[1m  6/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.2968 - loss: 1.8613
[36m(train_cnn_ray_tune pid=3216748)[0m 
[1m136/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m5s[0m 31ms/step - accuracy: 0.3116 - loss: 1.7086
[1m138/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m5s[0m 31ms/step - accuracy: 0.3116 - loss: 1.7078[32m [repeated 69x across cluster][0m
[36m(train_cnn_ray_tune pid=3216749)[0m 
[1m 83/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m8s[0m 35ms/step - accuracy: 0.2918 - loss: 1.7332[32m [repeated 79x across cluster][0m
[36m(train_cnn_ray_tune pid=3216756)[0m 
[1m 83/655[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m31s[0m 55ms/step - accuracy: 0.2814 - loss: 1.9277
[1m 84/655[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m31s[0m 55ms/step - accuracy: 0.2817 - loss: 1.9262[32m [repeated 195x across cluster][0m
[36m(train_cnn_ray_tune pid=3216747)[0m 
[1m 73/655[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m31s[0m 54ms/step - accuracy: 0.2634 - loss: 1.8550[32m [repeated 315x across cluster][0m
[36m(train_cnn_ray_tune pid=3216751)[0m 
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[36m(train_cnn_ray_tune pid=3216745)[0m 
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[36m(train_cnn_ray_tune pid=3216742)[0m 
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[36m(train_cnn_ray_tune pid=3216754)[0m 
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[36m(train_cnn_ray_tune pid=3216742)[0m 
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[36m(train_cnn_ray_tune pid=3216755)[0m 
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[36m(train_cnn_ray_tune pid=3216742)[0m 
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[36m(train_cnn_ray_tune pid=3216742)[0m 
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[36m(train_cnn_ray_tune pid=3216745)[0m 
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[36m(train_cnn_ray_tune pid=3216749)[0m 
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[36m(train_cnn_ray_tune pid=3216752)[0m 
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[36m(train_cnn_ray_tune pid=3216742)[0m 
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[36m(train_cnn_ray_tune pid=3216752)[0m 
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[36m(train_cnn_ray_tune pid=3216745)[0m 
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[36m(train_cnn_ray_tune pid=3216753)[0m 
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[36m(train_cnn_ray_tune pid=3216729)[0m 
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[36m(train_cnn_ray_tune pid=3216742)[0m 
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[36m(train_cnn_ray_tune pid=3216742)[0m 
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[36m(train_cnn_ray_tune pid=3216754)[0m 
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[36m(train_cnn_ray_tune pid=3216754)[0m Epoch 2/26
[36m(train_cnn_ray_tune pid=3216742)[0m 
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[36m(train_cnn_ray_tune pid=3216742)[0m 
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[36m(train_cnn_ray_tune pid=3216745)[0m 
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[36m(train_cnn_ray_tune pid=3216746)[0m 
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[36m(train_cnn_ray_tune pid=3216742)[0m 
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[36m(train_cnn_ray_tune pid=3216756)[0m 
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[36m(train_cnn_ray_tune pid=3216756)[0m 
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[36m(train_cnn_ray_tune pid=3216750)[0m 
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[36m(train_cnn_ray_tune pid=3216745)[0m 
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[36m(train_cnn_ray_tune pid=3216748)[0m 
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[36m(train_cnn_ray_tune pid=3216748)[0m 
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[36m(train_cnn_ray_tune pid=3216753)[0m 
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[36m(train_cnn_ray_tune pid=3216729)[0m 
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[36m(train_cnn_ray_tune pid=3216729)[0m 
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[36m(train_cnn_ray_tune pid=3216729)[0m Epoch 2/29[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3216743)[0m 
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[36m(train_cnn_ray_tune pid=3216749)[0m 
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[36m(train_cnn_ray_tune pid=3216751)[0m 
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Trial status: 20 RUNNING
Current time: 2025-11-07 16:37:28. 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     num_resblocks     tasa_aprendizaje     epochs │
├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_987ea    RUNNING            2   adam            tanh                                   16                 32                  3                 0          1.76369e-05         22 │
│ trial_987ea    RUNNING            3   adam            tanh                                   16                 64                  3                 1          0.000124674         27 │
│ trial_987ea    RUNNING            3   rmsprop         tanh                                   32                 16                  3                 1          0.0001207           28 │
│ trial_987ea    RUNNING            2   rmsprop         tanh                                   16                 64                  3                 1          8.8649e-06          23 │
│ trial_987ea    RUNNING            2   adam            tanh                                   32                 16                  3                 1          0.000103558         26 │
│ trial_987ea    RUNNING            2   rmsprop         tanh                                   32                 16                  5                 0          0.000131282         20 │
│ trial_987ea    RUNNING            2   rmsprop         relu                                   32                 32                  5                 0          4.30906e-05         29 │
│ trial_987ea    RUNNING            3   rmsprop         relu                                   32                 32                  3                 0          6.42207e-06         25 │
│ trial_987ea    RUNNING            3   rmsprop         relu                                   16                 64                  5                 1          6.20968e-06         17 │
│ trial_987ea    RUNNING            2   rmsprop         tanh                                   32                 16                  3                 0          2.39155e-05         26 │
│ trial_987ea    RUNNING            3   rmsprop         tanh                                   32                 32                  3                 0          2.14512e-05         19 │
│ trial_987ea    RUNNING            3   adam            relu                                   16                 64                  5                 0          3.57777e-05         28 │
│ trial_987ea    RUNNING            2   adam            relu                                   32                 16                  3                 0          4.86659e-05         23 │
│ trial_987ea    RUNNING            3   rmsprop         tanh                                   16                 64                  3                 1          4.71571e-05         22 │
│ trial_987ea    RUNNING            3   adam            relu                                   16                 64                  5                 0          9.94182e-05         27 │
│ trial_987ea    RUNNING            2   adam            tanh                                   16                 16                  3                 0          0.000100097         16 │
│ trial_987ea    RUNNING            2   adam            relu                                   32                 64                  3                 1          1.89531e-05         28 │
│ trial_987ea    RUNNING            3   adam            tanh                                   32                 64                  5                 1          0.000197693         21 │
│ trial_987ea    RUNNING            3   rmsprop         relu                                   16                 64                  5                 1          0.000102887         19 │
│ trial_987ea    RUNNING            2   rmsprop         relu                                   16                 16                  3                 0          4.74263e-05         18 │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3216745)[0m 
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[36m(train_cnn_ray_tune pid=3216741)[0m 
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[36m(train_cnn_ray_tune pid=3216756)[0m 
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[36m(train_cnn_ray_tune pid=3216757)[0m 
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[36m(train_cnn_ray_tune pid=3216751)[0m 
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[36m(train_cnn_ray_tune pid=3216742)[0m 
[36m(train_cnn_ray_tune pid=3216753)[0m 
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[36m(train_cnn_ray_tune pid=3216742)[0m 
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[36m(train_cnn_ray_tune pid=3216746)[0m 
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[36m(train_cnn_ray_tune pid=3216746)[0m Epoch 2/28[32m [repeated 3x across cluster][0m
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[36m(train_cnn_ray_tune pid=3216729)[0m 
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[36m(train_cnn_ray_tune pid=3216748)[0m 
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[36m(train_cnn_ray_tune pid=3216749)[0m 
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[36m(train_cnn_ray_tune pid=3216756)[0m 
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[36m(train_cnn_ray_tune pid=3216752)[0m 
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[36m(train_cnn_ray_tune pid=3216742)[0m 
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[36m(train_cnn_ray_tune pid=3216753)[0m 
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[36m(train_cnn_ray_tune pid=3216757)[0m 
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[36m(train_cnn_ray_tune pid=3216741)[0m 
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[36m(train_cnn_ray_tune pid=3216750)[0m 
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[36m(train_cnn_ray_tune pid=3216750)[0m Epoch 2/19[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=3216745)[0m 
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[36m(train_cnn_ray_tune pid=3216748)[0m 
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[36m(train_cnn_ray_tune pid=3216743)[0m 
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[36m(train_cnn_ray_tune pid=3216745)[0m 
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[36m(train_cnn_ray_tune pid=3216752)[0m 
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[36m(train_cnn_ray_tune pid=3216754)[0m 
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[36m(train_cnn_ray_tune pid=3216744)[0m 
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[36m(train_cnn_ray_tune pid=3216748)[0m 
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[36m(train_cnn_ray_tune pid=3216748)[0m Epoch 3/20[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3216748)[0m 
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[36m(train_cnn_ray_tune pid=3216723)[0m 
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[36m(train_cnn_ray_tune pid=3216754)[0m 
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[36m(train_cnn_ray_tune pid=3216744)[0m 
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[36m(train_cnn_ray_tune pid=3216756)[0m 
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[36m(train_cnn_ray_tune pid=3216752)[0m 
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[36m(train_cnn_ray_tune pid=3216756)[0m 
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[36m(train_cnn_ray_tune pid=3216751)[0m 
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[36m(train_cnn_ray_tune pid=3216748)[0m 
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[36m(train_cnn_ray_tune pid=3216748)[0m 
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[36m(train_cnn_ray_tune pid=3216751)[0m 
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[36m(train_cnn_ray_tune pid=3216751)[0m Epoch 3/23[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3216751)[0m 
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[36m(train_cnn_ray_tune pid=3216743)[0m 
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[36m(train_cnn_ray_tune pid=3216756)[0m 
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[36m(train_cnn_ray_tune pid=3216750)[0m 
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[36m(train_cnn_ray_tune pid=3216729)[0m 
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[36m(train_cnn_ray_tune pid=3216729)[0m 
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[36m(train_cnn_ray_tune pid=3216757)[0m 
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[36m(train_cnn_ray_tune pid=3216742)[0m 
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[36m(train_cnn_ray_tune pid=3216742)[0m 
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[36m(train_cnn_ray_tune pid=3216742)[0m 
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[36m(train_cnn_ray_tune pid=3216742)[0m 
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[36m(train_cnn_ray_tune pid=3216729)[0m 
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[36m(train_cnn_ray_tune pid=3216754)[0m 
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[36m(train_cnn_ray_tune pid=3216754)[0m Epoch 4/26[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3216744)[0m 
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[36m(train_cnn_ray_tune pid=3216744)[0m 
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[36m(train_cnn_ray_tune pid=3216745)[0m 
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[36m(train_cnn_ray_tune pid=3216756)[0m 
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[36m(train_cnn_ray_tune pid=3216752)[0m 
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[36m(train_cnn_ray_tune pid=3216755)[0m 
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[36m(train_cnn_ray_tune pid=3216744)[0m 
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[36m(train_cnn_ray_tune pid=3216746)[0m 
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[36m(train_cnn_ray_tune pid=3216753)[0m 
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[36m(train_cnn_ray_tune pid=3216744)[0m 
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[36m(train_cnn_ray_tune pid=3216744)[0m Epoch 3/28[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3216748)[0m 
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[36m(train_cnn_ray_tune pid=3216745)[0m 
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Trial status: 20 RUNNING
Current time: 2025-11-07 16:37:58. 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     num_resblocks     tasa_aprendizaje     epochs │
├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_987ea    RUNNING            2   adam            tanh                                   16                 32                  3                 0          1.76369e-05         22 │
│ trial_987ea    RUNNING            3   adam            tanh                                   16                 64                  3                 1          0.000124674         27 │
│ trial_987ea    RUNNING            3   rmsprop         tanh                                   32                 16                  3                 1          0.0001207           28 │
│ trial_987ea    RUNNING            2   rmsprop         tanh                                   16                 64                  3                 1          8.8649e-06          23 │
│ trial_987ea    RUNNING            2   adam            tanh                                   32                 16                  3                 1          0.000103558         26 │
│ trial_987ea    RUNNING            2   rmsprop         tanh                                   32                 16                  5                 0          0.000131282         20 │
│ trial_987ea    RUNNING            2   rmsprop         relu                                   32                 32                  5                 0          4.30906e-05         29 │
│ trial_987ea    RUNNING            3   rmsprop         relu                                   32                 32                  3                 0          6.42207e-06         25 │
│ trial_987ea    RUNNING            3   rmsprop         relu                                   16                 64                  5                 1          6.20968e-06         17 │
│ trial_987ea    RUNNING            2   rmsprop         tanh                                   32                 16                  3                 0          2.39155e-05         26 │
│ trial_987ea    RUNNING            3   rmsprop         tanh                                   32                 32                  3                 0          2.14512e-05         19 │
│ trial_987ea    RUNNING            3   adam            relu                                   16                 64                  5                 0          3.57777e-05         28 │
│ trial_987ea    RUNNING            2   adam            relu                                   32                 16                  3                 0          4.86659e-05         23 │
│ trial_987ea    RUNNING            3   rmsprop         tanh                                   16                 64                  3                 1          4.71571e-05         22 │
│ trial_987ea    RUNNING            3   adam            relu                                   16                 64                  5                 0          9.94182e-05         27 │
│ trial_987ea    RUNNING            2   adam            tanh                                   16                 16                  3                 0          0.000100097         16 │
│ trial_987ea    RUNNING            2   adam            relu                                   32                 64                  3                 1          1.89531e-05         28 │
│ trial_987ea    RUNNING            3   adam            tanh                                   32                 64                  5                 1          0.000197693         21 │
│ trial_987ea    RUNNING            3   rmsprop         relu                                   16                 64                  5                 1          0.000102887         19 │
│ trial_987ea    RUNNING            2   rmsprop         relu                                   16                 16                  3                 0          4.74263e-05         18 │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3216751)[0m 
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[36m(train_cnn_ray_tune pid=3216752)[0m 
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[36m(train_cnn_ray_tune pid=3216755)[0m 
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[36m(train_cnn_ray_tune pid=3216748)[0m 
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[36m(train_cnn_ray_tune pid=3216744)[0m 
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[36m(train_cnn_ray_tune pid=3216749)[0m 
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[36m(train_cnn_ray_tune pid=3216744)[0m 
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[36m(train_cnn_ray_tune pid=3216741)[0m 
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[36m(train_cnn_ray_tune pid=3216751)[0m 
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[36m(train_cnn_ray_tune pid=3216751)[0m 
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[36m(train_cnn_ray_tune pid=3216751)[0m Epoch 4/23[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=3216747)[0m 
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[36m(train_cnn_ray_tune pid=3216729)[0m 
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[36m(train_cnn_ray_tune pid=3216748)[0m 
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[36m(train_cnn_ray_tune pid=3216756)[0m 
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[36m(train_cnn_ray_tune pid=3216747)[0m 
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[36m(train_cnn_ray_tune pid=3216750)[0m 
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[36m(train_cnn_ray_tune pid=3216729)[0m 
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[36m(train_cnn_ray_tune pid=3216748)[0m 
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[36m(train_cnn_ray_tune pid=3216754)[0m 
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[36m(train_cnn_ray_tune pid=3216752)[0m 
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[36m(train_cnn_ray_tune pid=3216752)[0m Epoch 2/17[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3216748)[0m 
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[36m(train_cnn_ray_tune pid=3216751)[0m 
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[36m(train_cnn_ray_tune pid=3216747)[0m 
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[36m(train_cnn_ray_tune pid=3216752)[0m 
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[36m(train_cnn_ray_tune pid=3216750)[0m 
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[36m(train_cnn_ray_tune pid=3216741)[0m 
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[36m(train_cnn_ray_tune pid=3216746)[0m 
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[36m(train_cnn_ray_tune pid=3216740)[0m 
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[36m(train_cnn_ray_tune pid=3216740)[0m 
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[36m(train_cnn_ray_tune pid=3216740)[0m Epoch 2/27[32m [repeated 2x across cluster][0m
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[36m(train_cnn_ray_tune pid=3216745)[0m 
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[36m(train_cnn_ray_tune pid=3216757)[0m 
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[36m(train_cnn_ray_tune pid=3216748)[0m 
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[36m(train_cnn_ray_tune pid=3216745)[0m 
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[36m(train_cnn_ray_tune pid=3216741)[0m 
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[36m(train_cnn_ray_tune pid=3216756)[0m 
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[36m(train_cnn_ray_tune pid=3216752)[0m 
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[36m(train_cnn_ray_tune pid=3216745)[0m 
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[36m(train_cnn_ray_tune pid=3216744)[0m 
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[36m(train_cnn_ray_tune pid=3216751)[0m 
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[36m(train_cnn_ray_tune pid=3216751)[0m Epoch 5/23[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=3216749)[0m 
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[36m(train_cnn_ray_tune pid=3216723)[0m 
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[36m(train_cnn_ray_tune pid=3216755)[0m 
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[36m(train_cnn_ray_tune pid=3216745)[0m 
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[36m(train_cnn_ray_tune pid=3216742)[0m 
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[36m(train_cnn_ray_tune pid=3216754)[0m 
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[36m(train_cnn_ray_tune pid=3216729)[0m 
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[36m(train_cnn_ray_tune pid=3216745)[0m 
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[36m(train_cnn_ray_tune pid=3216746)[0m 
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[36m(train_cnn_ray_tune pid=3216723)[0m 
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[36m(train_cnn_ray_tune pid=3216729)[0m 
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[36m(train_cnn_ray_tune pid=3216752)[0m 
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[36m(train_cnn_ray_tune pid=3216729)[0m 
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[36m(train_cnn_ray_tune pid=3216743)[0m 
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[36m(train_cnn_ray_tune pid=3216743)[0m Epoch 3/23[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=3216754)[0m 
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[36m(train_cnn_ray_tune pid=3216750)[0m 
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[36m(train_cnn_ray_tune pid=3216744)[0m 
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[36m(train_cnn_ray_tune pid=3216743)[0m 
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[36m(train_cnn_ray_tune pid=3216741)[0m 
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[36m(train_cnn_ray_tune pid=3216748)[0m 
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[36m(train_cnn_ray_tune pid=3216746)[0m 
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[36m(train_cnn_ray_tune pid=3216729)[0m 
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[36m(train_cnn_ray_tune pid=3216753)[0m 
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[36m(train_cnn_ray_tune pid=3216756)[0m 
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[36m(train_cnn_ray_tune pid=3216745)[0m 
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[36m(train_cnn_ray_tune pid=3216745)[0m 
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[36m(train_cnn_ray_tune pid=3216745)[0m 
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[36m(train_cnn_ray_tune pid=3216745)[0m Epoch 4/18[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3216756)[0m 
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[36m(train_cnn_ray_tune pid=3216748)[0m 
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Trial status: 20 RUNNING
Current time: 2025-11-07 16:38:28. 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     num_resblocks     tasa_aprendizaje     epochs │
├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_987ea    RUNNING            2   adam            tanh                                   16                 32                  3                 0          1.76369e-05         22 │
│ trial_987ea    RUNNING            3   adam            tanh                                   16                 64                  3                 1          0.000124674         27 │
│ trial_987ea    RUNNING            3   rmsprop         tanh                                   32                 16                  3                 1          0.0001207           28 │
│ trial_987ea    RUNNING            2   rmsprop         tanh                                   16                 64                  3                 1          8.8649e-06          23 │
│ trial_987ea    RUNNING            2   adam            tanh                                   32                 16                  3                 1          0.000103558         26 │
│ trial_987ea    RUNNING            2   rmsprop         tanh                                   32                 16                  5                 0          0.000131282         20 │
│ trial_987ea    RUNNING            2   rmsprop         relu                                   32                 32                  5                 0          4.30906e-05         29 │
│ trial_987ea    RUNNING            3   rmsprop         relu                                   32                 32                  3                 0          6.42207e-06         25 │
│ trial_987ea    RUNNING            3   rmsprop         relu                                   16                 64                  5                 1          6.20968e-06         17 │
│ trial_987ea    RUNNING            2   rmsprop         tanh                                   32                 16                  3                 0          2.39155e-05         26 │
│ trial_987ea    RUNNING            3   rmsprop         tanh                                   32                 32                  3                 0          2.14512e-05         19 │
│ trial_987ea    RUNNING            3   adam            relu                                   16                 64                  5                 0          3.57777e-05         28 │
│ trial_987ea    RUNNING            2   adam            relu                                   32                 16                  3                 0          4.86659e-05         23 │
│ trial_987ea    RUNNING            3   rmsprop         tanh                                   16                 64                  3                 1          4.71571e-05         22 │
│ trial_987ea    RUNNING            3   adam            relu                                   16                 64                  5                 0          9.94182e-05         27 │
│ trial_987ea    RUNNING            2   adam            tanh                                   16                 16                  3                 0          0.000100097         16 │
│ trial_987ea    RUNNING            2   adam            relu                                   32                 64                  3                 1          1.89531e-05         28 │
│ trial_987ea    RUNNING            3   adam            tanh                                   32                 64                  5                 1          0.000197693         21 │
│ trial_987ea    RUNNING            3   rmsprop         relu                                   16                 64                  5                 1          0.000102887         19 │
│ trial_987ea    RUNNING            2   rmsprop         relu                                   16                 16                  3                 0          4.74263e-05         18 │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3216751)[0m 
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[36m(train_cnn_ray_tune pid=3216744)[0m 
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[36m(train_cnn_ray_tune pid=3216745)[0m 
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[36m(train_cnn_ray_tune pid=3216752)[0m 
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[36m(train_cnn_ray_tune pid=3216749)[0m 
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[36m(train_cnn_ray_tune pid=3216746)[0m 
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[36m(train_cnn_ray_tune pid=3216746)[0m Epoch 5/28[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3216744)[0m 
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[36m(train_cnn_ray_tune pid=3216748)[0m 
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[36m(train_cnn_ray_tune pid=3216748)[0m 
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[36m(train_cnn_ray_tune pid=3216729)[0m 
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[36m(train_cnn_ray_tune pid=3216754)[0m 
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[36m(train_cnn_ray_tune pid=3216753)[0m 
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[36m(train_cnn_ray_tune pid=3216748)[0m 
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[36m(train_cnn_ray_tune pid=3216723)[0m 
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[36m(train_cnn_ray_tune pid=3216745)[0m 
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[36m(train_cnn_ray_tune pid=3216752)[0m 
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[36m(train_cnn_ray_tune pid=3216754)[0m 
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[36m(train_cnn_ray_tune pid=3216751)[0m 
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[36m(train_cnn_ray_tune pid=3216729)[0m 
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[36m(train_cnn_ray_tune pid=3216729)[0m 
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[36m(train_cnn_ray_tune pid=3216729)[0m Epoch 6/29[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=3216754)[0m 
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[36m(train_cnn_ray_tune pid=3216751)[0m 
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[36m(train_cnn_ray_tune pid=3216748)[0m 
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[36m(train_cnn_ray_tune pid=3216754)[0m 
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[36m(train_cnn_ray_tune pid=3216740)[0m 
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[36m(train_cnn_ray_tune pid=3216752)[0m 
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[36m(train_cnn_ray_tune pid=3216749)[0m 
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[36m(train_cnn_ray_tune pid=3216756)[0m 
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[36m(train_cnn_ray_tune pid=3216745)[0m 
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[36m(train_cnn_ray_tune pid=3216754)[0m 
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[36m(train_cnn_ray_tune pid=3216748)[0m 
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[36m(train_cnn_ray_tune pid=3216748)[0m Epoch 7/20
[36m(train_cnn_ray_tune pid=3216748)[0m 
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[36m(train_cnn_ray_tune pid=3216748)[0m 
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[36m(train_cnn_ray_tune pid=3216740)[0m 
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[36m(train_cnn_ray_tune pid=3216744)[0m 
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[36m(train_cnn_ray_tune pid=3216749)[0m 
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[36m(train_cnn_ray_tune pid=3216723)[0m 
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[36m(train_cnn_ray_tune pid=3216752)[0m 
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[36m(train_cnn_ray_tune pid=3216748)[0m 
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[36m(train_cnn_ray_tune pid=3216741)[0m 
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[36m(train_cnn_ray_tune pid=3216741)[0m Epoch 5/25[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=3216741)[0m 
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[36m(train_cnn_ray_tune pid=3216741)[0m 
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[36m(train_cnn_ray_tune pid=3216740)[0m 
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[36m(train_cnn_ray_tune pid=3216723)[0m 
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[36m(train_cnn_ray_tune pid=3216746)[0m 
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[36m(train_cnn_ray_tune pid=3216742)[0m 
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[36m(train_cnn_ray_tune pid=3216754)[0m 
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[36m(train_cnn_ray_tune pid=3216750)[0m 
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[36m(train_cnn_ray_tune pid=3216745)[0m 
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[36m(train_cnn_ray_tune pid=3216752)[0m 
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[36m(train_cnn_ray_tune pid=3216748)[0m 
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[36m(train_cnn_ray_tune pid=3216729)[0m 
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[36m(train_cnn_ray_tune pid=3216729)[0m Epoch 7/29[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=3216729)[0m 
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[36m(train_cnn_ray_tune pid=3216729)[0m 
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[36m(train_cnn_ray_tune pid=3216745)[0m 
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[36m(train_cnn_ray_tune pid=3216750)[0m 
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[36m(train_cnn_ray_tune pid=3216750)[0m 
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[36m(train_cnn_ray_tune pid=3216747)[0m 
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[36m(train_cnn_ray_tune pid=3216723)[0m 
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[36m(train_cnn_ray_tune pid=3216743)[0m 
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[36m(train_cnn_ray_tune pid=3216756)[0m 
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Trial status: 20 RUNNING
Current time: 2025-11-07 16:38:58. 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     num_resblocks     tasa_aprendizaje     epochs │
├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_987ea    RUNNING            2   adam            tanh                                   16                 32                  3                 0          1.76369e-05         22 │
│ trial_987ea    RUNNING            3   adam            tanh                                   16                 64                  3                 1          0.000124674         27 │
│ trial_987ea    RUNNING            3   rmsprop         tanh                                   32                 16                  3                 1          0.0001207           28 │
│ trial_987ea    RUNNING            2   rmsprop         tanh                                   16                 64                  3                 1          8.8649e-06          23 │
│ trial_987ea    RUNNING            2   adam            tanh                                   32                 16                  3                 1          0.000103558         26 │
│ trial_987ea    RUNNING            2   rmsprop         tanh                                   32                 16                  5                 0          0.000131282         20 │
│ trial_987ea    RUNNING            2   rmsprop         relu                                   32                 32                  5                 0          4.30906e-05         29 │
│ trial_987ea    RUNNING            3   rmsprop         relu                                   32                 32                  3                 0          6.42207e-06         25 │
│ trial_987ea    RUNNING            3   rmsprop         relu                                   16                 64                  5                 1          6.20968e-06         17 │
│ trial_987ea    RUNNING            2   rmsprop         tanh                                   32                 16                  3                 0          2.39155e-05         26 │
│ trial_987ea    RUNNING            3   rmsprop         tanh                                   32                 32                  3                 0          2.14512e-05         19 │
│ trial_987ea    RUNNING            3   adam            relu                                   16                 64                  5                 0          3.57777e-05         28 │
│ trial_987ea    RUNNING            2   adam            relu                                   32                 16                  3                 0          4.86659e-05         23 │
│ trial_987ea    RUNNING            3   rmsprop         tanh                                   16                 64                  3                 1          4.71571e-05         22 │
│ trial_987ea    RUNNING            3   adam            relu                                   16                 64                  5                 0          9.94182e-05         27 │
│ trial_987ea    RUNNING            2   adam            tanh                                   16                 16                  3                 0          0.000100097         16 │
│ trial_987ea    RUNNING            2   adam            relu                                   32                 64                  3                 1          1.89531e-05         28 │
│ trial_987ea    RUNNING            3   adam            tanh                                   32                 64                  5                 1          0.000197693         21 │
│ trial_987ea    RUNNING            3   rmsprop         relu                                   16                 64                  5                 1          0.000102887         19 │
│ trial_987ea    RUNNING            2   rmsprop         relu                                   16                 16                  3                 0          4.74263e-05         18 │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3216754)[0m 
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[36m(train_cnn_ray_tune pid=3216729)[0m 
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[36m(train_cnn_ray_tune pid=3216750)[0m 
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[36m(train_cnn_ray_tune pid=3216756)[0m 
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[36m(train_cnn_ray_tune pid=3216756)[0m Epoch 3/19[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3216756)[0m 
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[36m(train_cnn_ray_tune pid=3216747)[0m 
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[36m(train_cnn_ray_tune pid=3216747)[0m 
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[36m(train_cnn_ray_tune pid=3216756)[0m 
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[36m(train_cnn_ray_tune pid=3216748)[0m 
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[36m(train_cnn_ray_tune pid=3216757)[0m 
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[36m(train_cnn_ray_tune pid=3216756)[0m 
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[36m(train_cnn_ray_tune pid=3216748)[0m 
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[36m(train_cnn_ray_tune pid=3216755)[0m 
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[36m(train_cnn_ray_tune pid=3216754)[0m 
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[36m(train_cnn_ray_tune pid=3216754)[0m Epoch 9/26[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=3216754)[0m 
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[36m(train_cnn_ray_tune pid=3216752)[0m 
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[36m(train_cnn_ray_tune pid=3216754)[0m 
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[36m(train_cnn_ray_tune pid=3216754)[0m 
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[36m(train_cnn_ray_tune pid=3216749)[0m 
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[36m(train_cnn_ray_tune pid=3216756)[0m 
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[36m(train_cnn_ray_tune pid=3216748)[0m 
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[36m(train_cnn_ray_tune pid=3216751)[0m 
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[36m(train_cnn_ray_tune pid=3216740)[0m 
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[36m(train_cnn_ray_tune pid=3216756)[0m 
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[36m(train_cnn_ray_tune pid=3216745)[0m 
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[36m(train_cnn_ray_tune pid=3216748)[0m 
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[36m(train_cnn_ray_tune pid=3216750)[0m 
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[36m(train_cnn_ray_tune pid=3216750)[0m Epoch 6/19[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3216750)[0m 
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[36m(train_cnn_ray_tune pid=3216747)[0m 
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[36m(train_cnn_ray_tune pid=3216743)[0m 
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[36m(train_cnn_ray_tune pid=3216742)[0m 
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[36m(train_cnn_ray_tune pid=3216729)[0m 
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[36m(train_cnn_ray_tune pid=3216746)[0m 
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[36m(train_cnn_ray_tune pid=3216741)[0m 
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[36m(train_cnn_ray_tune pid=3216751)[0m 
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[36m(train_cnn_ray_tune pid=3216757)[0m 
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[36m(train_cnn_ray_tune pid=3216741)[0m 
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[36m(train_cnn_ray_tune pid=3216745)[0m 
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[36m(train_cnn_ray_tune pid=3216753)[0m 
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[36m(train_cnn_ray_tune pid=3216748)[0m 
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[36m(train_cnn_ray_tune pid=3216748)[0m Epoch 9/20[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=3216748)[0m 
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[36m(train_cnn_ray_tune pid=3216748)[0m 
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[36m(train_cnn_ray_tune pid=3216752)[0m 
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[36m(train_cnn_ray_tune pid=3216748)[0m 
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[36m(train_cnn_ray_tune pid=3216753)[0m 
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[36m(train_cnn_ray_tune pid=3216754)[0m 
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[36m(train_cnn_ray_tune pid=3216751)[0m 
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[36m(train_cnn_ray_tune pid=3216750)[0m 
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[36m(train_cnn_ray_tune pid=3216751)[0m 
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[36m(train_cnn_ray_tune pid=3216753)[0m 
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[36m(train_cnn_ray_tune pid=3216753)[0m Epoch 5/16[32m [repeated 5x across cluster][0m
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[36m(train_cnn_ray_tune pid=3216745)[0m 
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[36m(train_cnn_ray_tune pid=3216752)[0m 
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[36m(train_cnn_ray_tune pid=3216745)[0m 
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[36m(train_cnn_ray_tune pid=3216751)[0m 
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[36m(train_cnn_ray_tune pid=3216723)[0m 
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[36m(train_cnn_ray_tune pid=3216744)[0m 
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[36m(train_cnn_ray_tune pid=3216748)[0m 
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[36m(train_cnn_ray_tune pid=3216742)[0m 
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[36m(train_cnn_ray_tune pid=3216742)[0m Epoch 5/21[32m [repeated 3x across cluster][0m
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[36m(train_cnn_ray_tune pid=3216752)[0m 
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[36m(train_cnn_ray_tune pid=3216756)[0m 
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[36m(train_cnn_ray_tune pid=3216751)[0m 
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[36m(train_cnn_ray_tune pid=3216742)[0m 
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[36m(train_cnn_ray_tune pid=3216729)[0m 
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[36m(train_cnn_ray_tune pid=3216742)[0m 
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[36m(train_cnn_ray_tune pid=3216742)[0m 
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Trial status: 20 RUNNING
Current time: 2025-11-07 16:39:28. 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     num_resblocks     tasa_aprendizaje     epochs │
├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_987ea    RUNNING            2   adam            tanh                                   16                 32                  3                 0          1.76369e-05         22 │
│ trial_987ea    RUNNING            3   adam            tanh                                   16                 64                  3                 1          0.000124674         27 │
│ trial_987ea    RUNNING            3   rmsprop         tanh                                   32                 16                  3                 1          0.0001207           28 │
│ trial_987ea    RUNNING            2   rmsprop         tanh                                   16                 64                  3                 1          8.8649e-06          23 │
│ trial_987ea    RUNNING            2   adam            tanh                                   32                 16                  3                 1          0.000103558         26 │
│ trial_987ea    RUNNING            2   rmsprop         tanh                                   32                 16                  5                 0          0.000131282         20 │
│ trial_987ea    RUNNING            2   rmsprop         relu                                   32                 32                  5                 0          4.30906e-05         29 │
│ trial_987ea    RUNNING            3   rmsprop         relu                                   32                 32                  3                 0          6.42207e-06         25 │
│ trial_987ea    RUNNING            3   rmsprop         relu                                   16                 64                  5                 1          6.20968e-06         17 │
│ trial_987ea    RUNNING            2   rmsprop         tanh                                   32                 16                  3                 0          2.39155e-05         26 │
│ trial_987ea    RUNNING            3   rmsprop         tanh                                   32                 32                  3                 0          2.14512e-05         19 │
│ trial_987ea    RUNNING            3   adam            relu                                   16                 64                  5                 0          3.57777e-05         28 │
│ trial_987ea    RUNNING            2   adam            relu                                   32                 16                  3                 0          4.86659e-05         23 │
│ trial_987ea    RUNNING            3   rmsprop         tanh                                   16                 64                  3                 1          4.71571e-05         22 │
│ trial_987ea    RUNNING            3   adam            relu                                   16                 64                  5                 0          9.94182e-05         27 │
│ trial_987ea    RUNNING            2   adam            tanh                                   16                 16                  3                 0          0.000100097         16 │
│ trial_987ea    RUNNING            2   adam            relu                                   32                 64                  3                 1          1.89531e-05         28 │
│ trial_987ea    RUNNING            3   adam            tanh                                   32                 64                  5                 1          0.000197693         21 │
│ trial_987ea    RUNNING            3   rmsprop         relu                                   16                 64                  5                 1          0.000102887         19 │
│ trial_987ea    RUNNING            2   rmsprop         relu                                   16                 16                  3                 0          4.74263e-05         18 │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3216749)[0m 
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[36m(train_cnn_ray_tune pid=3216754)[0m 
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[36m(train_cnn_ray_tune pid=3216754)[0m 
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[36m(train_cnn_ray_tune pid=3216729)[0m 
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[36m(train_cnn_ray_tune pid=3216729)[0m Epoch 9/29
[36m(train_cnn_ray_tune pid=3216750)[0m 
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[36m(train_cnn_ray_tune pid=3216748)[0m 
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[36m(train_cnn_ray_tune pid=3216748)[0m Epoch 10/20
[36m(train_cnn_ray_tune pid=3216748)[0m 
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[36m(train_cnn_ray_tune pid=3216752)[0m 
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[36m(train_cnn_ray_tune pid=3216756)[0m 
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[36m(train_cnn_ray_tune pid=3216747)[0m 
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[36m(train_cnn_ray_tune pid=3216743)[0m 
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[36m(train_cnn_ray_tune pid=3216748)[0m 
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[36m(train_cnn_ray_tune pid=3216746)[0m 
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[36m(train_cnn_ray_tune pid=3216750)[0m 
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[36m(train_cnn_ray_tune pid=3216749)[0m 
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[36m(train_cnn_ray_tune pid=3216754)[0m 
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[36m(train_cnn_ray_tune pid=3216729)[0m 
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[36m(train_cnn_ray_tune pid=3216747)[0m 
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[36m(train_cnn_ray_tune pid=3216744)[0m 
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[36m(train_cnn_ray_tune pid=3216744)[0m Epoch 8/28[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=3216749)[0m 
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[36m(train_cnn_ray_tune pid=3216749)[0m 
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[36m(train_cnn_ray_tune pid=3216746)[0m 
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[36m(train_cnn_ray_tune pid=3216753)[0m 
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[36m(train_cnn_ray_tune pid=3216729)[0m 
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[36m(train_cnn_ray_tune pid=3216744)[0m 
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[36m(train_cnn_ray_tune pid=3216751)[0m 
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[36m(train_cnn_ray_tune pid=3216753)[0m 
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[36m(train_cnn_ray_tune pid=3216729)[0m 
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[36m(train_cnn_ray_tune pid=3216751)[0m 
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[36m(train_cnn_ray_tune pid=3216751)[0m Epoch 10/23[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3216751)[0m 
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[36m(train_cnn_ray_tune pid=3216755)[0m 
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[36m(train_cnn_ray_tune pid=3216752)[0m 
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[36m(train_cnn_ray_tune pid=3216749)[0m 
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[36m(train_cnn_ray_tune pid=3216756)[0m 
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[36m(train_cnn_ray_tune pid=3216744)[0m 
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[36m(train_cnn_ray_tune pid=3216751)[0m 
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[36m(train_cnn_ray_tune pid=3216752)[0m 
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[36m(train_cnn_ray_tune pid=3216729)[0m 
[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m47s[0m 144ms/step - accuracy: 0.5625 - loss: 1.1666
[36m(train_cnn_ray_tune pid=3216729)[0m 
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[36m(train_cnn_ray_tune pid=3216729)[0m Epoch 10/29
[36m(train_cnn_ray_tune pid=3216729)[0m 
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[36m(train_cnn_ray_tune pid=3216755)[0m 
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[36m(train_cnn_ray_tune pid=3216729)[0m 
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[36m(train_cnn_ray_tune pid=3216745)[0m 
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[36m(train_cnn_ray_tune pid=3216745)[0m Epoch 7/18
[36m(train_cnn_ray_tune pid=3216745)[0m 
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[36m(train_cnn_ray_tune pid=3216723)[0m 
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[36m(train_cnn_ray_tune pid=3216748)[0m 
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[36m(train_cnn_ray_tune pid=3216747)[0m 
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[36m(train_cnn_ray_tune pid=3216729)[0m 
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[36m(train_cnn_ray_tune pid=3216729)[0m 
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[36m(train_cnn_ray_tune pid=3216753)[0m 
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[36m(train_cnn_ray_tune pid=3216750)[0m 
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[36m(train_cnn_ray_tune pid=3216746)[0m 
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[36m(train_cnn_ray_tune pid=3216753)[0m 
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[36m(train_cnn_ray_tune pid=3216745)[0m 
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[36m(train_cnn_ray_tune pid=3216749)[0m 
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[36m(train_cnn_ray_tune pid=3216749)[0m 
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[36m(train_cnn_ray_tune pid=3216749)[0m Epoch 11/26[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3216747)[0m 
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[36m(train_cnn_ray_tune pid=3216744)[0m 
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[36m(train_cnn_ray_tune pid=3216749)[0m 
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[36m(train_cnn_ray_tune pid=3216757)[0m 
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[36m(train_cnn_ray_tune pid=3216741)[0m 
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[36m(train_cnn_ray_tune pid=3216723)[0m 
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[36m(train_cnn_ray_tune pid=3216744)[0m 
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[36m(train_cnn_ray_tune pid=3216742)[0m 
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[36m(train_cnn_ray_tune pid=3216729)[0m 
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[36m(train_cnn_ray_tune pid=3216740)[0m 
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[36m(train_cnn_ray_tune pid=3216742)[0m 
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[36m(train_cnn_ray_tune pid=3216745)[0m 
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[36m(train_cnn_ray_tune pid=3216753)[0m 
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[36m(train_cnn_ray_tune pid=3216742)[0m 
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[36m(train_cnn_ray_tune pid=3216742)[0m Epoch 6/21[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=3216748)[0m 
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[36m(train_cnn_ray_tune pid=3216753)[0m 
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Trial status: 20 RUNNING
Current time: 2025-11-07 16:39:58. 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     num_resblocks     tasa_aprendizaje     epochs │
├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_987ea    RUNNING            2   adam            tanh                                   16                 32                  3                 0          1.76369e-05         22 │
│ trial_987ea    RUNNING            3   adam            tanh                                   16                 64                  3                 1          0.000124674         27 │
│ trial_987ea    RUNNING            3   rmsprop         tanh                                   32                 16                  3                 1          0.0001207           28 │
│ trial_987ea    RUNNING            2   rmsprop         tanh                                   16                 64                  3                 1          8.8649e-06          23 │
│ trial_987ea    RUNNING            2   adam            tanh                                   32                 16                  3                 1          0.000103558         26 │
│ trial_987ea    RUNNING            2   rmsprop         tanh                                   32                 16                  5                 0          0.000131282         20 │
│ trial_987ea    RUNNING            2   rmsprop         relu                                   32                 32                  5                 0          4.30906e-05         29 │
│ trial_987ea    RUNNING            3   rmsprop         relu                                   32                 32                  3                 0          6.42207e-06         25 │
│ trial_987ea    RUNNING            3   rmsprop         relu                                   16                 64                  5                 1          6.20968e-06         17 │
│ trial_987ea    RUNNING            2   rmsprop         tanh                                   32                 16                  3                 0          2.39155e-05         26 │
│ trial_987ea    RUNNING            3   rmsprop         tanh                                   32                 32                  3                 0          2.14512e-05         19 │
│ trial_987ea    RUNNING            3   adam            relu                                   16                 64                  5                 0          3.57777e-05         28 │
│ trial_987ea    RUNNING            2   adam            relu                                   32                 16                  3                 0          4.86659e-05         23 │
│ trial_987ea    RUNNING            3   rmsprop         tanh                                   16                 64                  3                 1          4.71571e-05         22 │
│ trial_987ea    RUNNING            3   adam            relu                                   16                 64                  5                 0          9.94182e-05         27 │
│ trial_987ea    RUNNING            2   adam            tanh                                   16                 16                  3                 0          0.000100097         16 │
│ trial_987ea    RUNNING            2   adam            relu                                   32                 64                  3                 1          1.89531e-05         28 │
│ trial_987ea    RUNNING            3   adam            tanh                                   32                 64                  5                 1          0.000197693         21 │
│ trial_987ea    RUNNING            3   rmsprop         relu                                   16                 64                  5                 1          0.000102887         19 │
│ trial_987ea    RUNNING            2   rmsprop         relu                                   16                 16                  3                 0          4.74263e-05         18 │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3216752)[0m 
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[36m(train_cnn_ray_tune pid=3216750)[0m 
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[36m(train_cnn_ray_tune pid=3216745)[0m 
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[36m(train_cnn_ray_tune pid=3216729)[0m 
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[36m(train_cnn_ray_tune pid=3216752)[0m 
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[36m(train_cnn_ray_tune pid=3216756)[0m 
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[36m(train_cnn_ray_tune pid=3216752)[0m 
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[36m(train_cnn_ray_tune pid=3216752)[0m 
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[36m(train_cnn_ray_tune pid=3216752)[0m Epoch 4/17[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3216748)[0m 
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[36m(train_cnn_ray_tune pid=3216740)[0m 
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[36m(train_cnn_ray_tune pid=3216740)[0m 
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[36m(train_cnn_ray_tune pid=3216755)[0m 
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[36m(train_cnn_ray_tune pid=3216740)[0m 
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[36m(train_cnn_ray_tune pid=3216745)[0m 
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[36m(train_cnn_ray_tune pid=3216753)[0m 
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[36m(train_cnn_ray_tune pid=3216756)[0m 
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[36m(train_cnn_ray_tune pid=3216752)[0m 
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[36m(train_cnn_ray_tune pid=3216749)[0m 
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[36m(train_cnn_ray_tune pid=3216749)[0m Epoch 12/26[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3216740)[0m 
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[36m(train_cnn_ray_tune pid=3216751)[0m 
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[36m(train_cnn_ray_tune pid=3216744)[0m 
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[36m(train_cnn_ray_tune pid=3216744)[0m Epoch 10/28[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3216751)[0m 
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[36m(train_cnn_ray_tune pid=3216729)[0m 
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[36m(train_cnn_ray_tune pid=3216755)[0m 
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[36m(train_cnn_ray_tune pid=3216748)[0m 
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[36m(train_cnn_ray_tune pid=3216751)[0m 
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[36m(train_cnn_ray_tune pid=3216741)[0m 
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[36m(train_cnn_ray_tune pid=3216745)[0m 
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[36m(train_cnn_ray_tune pid=3216752)[0m 
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[36m(train_cnn_ray_tune pid=3216744)[0m 
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[36m(train_cnn_ray_tune pid=3216755)[0m 
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[36m(train_cnn_ray_tune pid=3216755)[0m Epoch 4/28[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=3216755)[0m 
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[36m(train_cnn_ray_tune pid=3216729)[0m 
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[36m(train_cnn_ray_tune pid=3216755)[0m 
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[36m(train_cnn_ray_tune pid=3216742)[0m 
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[36m(train_cnn_ray_tune pid=3216745)[0m 
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[36m(train_cnn_ray_tune pid=3216751)[0m 
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[36m(train_cnn_ray_tune pid=3216756)[0m 
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[36m(train_cnn_ray_tune pid=3216756)[0m 
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[36m(train_cnn_ray_tune pid=3216752)[0m 
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[36m(train_cnn_ray_tune pid=3216729)[0m 
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[36m(train_cnn_ray_tune pid=3216723)[0m 
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[36m(train_cnn_ray_tune pid=3216723)[0m Epoch 7/22[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3216729)[0m 
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Trial status: 20 RUNNING
Current time: 2025-11-07 16:40:28. 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     num_resblocks     tasa_aprendizaje     epochs │
├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_987ea    RUNNING            2   adam            tanh                                   16                 32                  3                 0          1.76369e-05         22 │
│ trial_987ea    RUNNING            3   adam            tanh                                   16                 64                  3                 1          0.000124674         27 │
│ trial_987ea    RUNNING            3   rmsprop         tanh                                   32                 16                  3                 1          0.0001207           28 │
│ trial_987ea    RUNNING            2   rmsprop         tanh                                   16                 64                  3                 1          8.8649e-06          23 │
│ trial_987ea    RUNNING            2   adam            tanh                                   32                 16                  3                 1          0.000103558         26 │
│ trial_987ea    RUNNING            2   rmsprop         tanh                                   32                 16                  5                 0          0.000131282         20 │
│ trial_987ea    RUNNING            2   rmsprop         relu                                   32                 32                  5                 0          4.30906e-05         29 │
│ trial_987ea    RUNNING            3   rmsprop         relu                                   32                 32                  3                 0          6.42207e-06         25 │
│ trial_987ea    RUNNING            3   rmsprop         relu                                   16                 64                  5                 1          6.20968e-06         17 │
│ trial_987ea    RUNNING            2   rmsprop         tanh                                   32                 16                  3                 0          2.39155e-05         26 │
│ trial_987ea    RUNNING            3   rmsprop         tanh                                   32                 32                  3                 0          2.14512e-05         19 │
│ trial_987ea    RUNNING            3   adam            relu                                   16                 64                  5                 0          3.57777e-05         28 │
│ trial_987ea    RUNNING            2   adam            relu                                   32                 16                  3                 0          4.86659e-05         23 │
│ trial_987ea    RUNNING            3   rmsprop         tanh                                   16                 64                  3                 1          4.71571e-05         22 │
│ trial_987ea    RUNNING            3   adam            relu                                   16                 64                  5                 0          9.94182e-05         27 │
│ trial_987ea    RUNNING            2   adam            tanh                                   16                 16                  3                 0          0.000100097         16 │
│ trial_987ea    RUNNING            2   adam            relu                                   32                 64                  3                 1          1.89531e-05         28 │
│ trial_987ea    RUNNING            3   adam            tanh                                   32                 64                  5                 1          0.000197693         21 │
│ trial_987ea    RUNNING            3   rmsprop         relu                                   16                 64                  5                 1          0.000102887         19 │
│ trial_987ea    RUNNING            2   rmsprop         relu                                   16                 16                  3                 0          4.74263e-05         18 │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3216754)[0m 
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[36m(train_cnn_ray_tune pid=3216729)[0m 
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[36m(train_cnn_ray_tune pid=3216745)[0m 
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[36m(train_cnn_ray_tune pid=3216756)[0m 
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[36m(train_cnn_ray_tune pid=3216723)[0m 
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[36m(train_cnn_ray_tune pid=3216755)[0m 
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[36m(train_cnn_ray_tune pid=3216740)[0m 
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[36m(train_cnn_ray_tune pid=3216741)[0m 
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[36m(train_cnn_ray_tune pid=3216751)[0m 
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[36m(train_cnn_ray_tune pid=3216751)[0m Epoch 13/23[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3216748)[0m 
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[36m(train_cnn_ray_tune pid=3216749)[0m 
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[36m(train_cnn_ray_tune pid=3216749)[0m 
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[36m(train_cnn_ray_tune pid=3216747)[0m 
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[36m(train_cnn_ray_tune pid=3216748)[0m 
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[36m(train_cnn_ray_tune pid=3216748)[0m 
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[36m(train_cnn_ray_tune pid=3216752)[0m 
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[36m(train_cnn_ray_tune pid=3216755)[0m 
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[36m(train_cnn_ray_tune pid=3216746)[0m 
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[36m(train_cnn_ray_tune pid=3216757)[0m 
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[36m(train_cnn_ray_tune pid=3216745)[0m 
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[36m(train_cnn_ray_tune pid=3216745)[0m Epoch 9/18[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=3216729)[0m 
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[36m(train_cnn_ray_tune pid=3216757)[0m 
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[36m(train_cnn_ray_tune pid=3216757)[0m 
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[36m(train_cnn_ray_tune pid=3216757)[0m 
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[36m(train_cnn_ray_tune pid=3216757)[0m 
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[36m(train_cnn_ray_tune pid=3216752)[0m 
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[36m(train_cnn_ray_tune pid=3216745)[0m 
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[36m(train_cnn_ray_tune pid=3216748)[0m 
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[36m(train_cnn_ray_tune pid=3216748)[0m 
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[36m(train_cnn_ray_tune pid=3216752)[0m 
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[36m(train_cnn_ray_tune pid=3216750)[0m 
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[36m(train_cnn_ray_tune pid=3216743)[0m 
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[36m(train_cnn_ray_tune pid=3216745)[0m 
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[36m(train_cnn_ray_tune pid=3216723)[0m 
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[36m(train_cnn_ray_tune pid=3216743)[0m 
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[36m(train_cnn_ray_tune pid=3216743)[0m Epoch 7/23[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3216745)[0m 
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[36m(train_cnn_ray_tune pid=3216751)[0m 
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[36m(train_cnn_ray_tune pid=3216723)[0m 
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[36m(train_cnn_ray_tune pid=3216752)[0m 
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[36m(train_cnn_ray_tune pid=3216723)[0m 
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[36m(train_cnn_ray_tune pid=3216754)[0m 
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[36m(train_cnn_ray_tune pid=3216747)[0m 
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[36m(train_cnn_ray_tune pid=3216754)[0m 
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[36m(train_cnn_ray_tune pid=3216756)[0m 
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[36m(train_cnn_ray_tune pid=3216756)[0m Epoch 5/19[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3216756)[0m 
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[36m(train_cnn_ray_tune pid=3216756)[0m 
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[36m(train_cnn_ray_tune pid=3216750)[0m 
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[36m(train_cnn_ray_tune pid=3216754)[0m 
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[36m(train_cnn_ray_tune pid=3216729)[0m 
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[36m(train_cnn_ray_tune pid=3216745)[0m 
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[36m(train_cnn_ray_tune pid=3216748)[0m 
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[36m(train_cnn_ray_tune pid=3216749)[0m 
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[36m(train_cnn_ray_tune pid=3216746)[0m 
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[36m(train_cnn_ray_tune pid=3216746)[0m 
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[36m(train_cnn_ray_tune pid=3216747)[0m 
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[36m(train_cnn_ray_tune pid=3216743)[0m 
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[36m(train_cnn_ray_tune pid=3216751)[0m 
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[36m(train_cnn_ray_tune pid=3216751)[0m Epoch 14/23[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=3216751)[0m 
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[36m(train_cnn_ray_tune pid=3216729)[0m 
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[36m(train_cnn_ray_tune pid=3216748)[0m 
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[36m(train_cnn_ray_tune pid=3216752)[0m 
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[36m(train_cnn_ray_tune pid=3216744)[0m 
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[36m(train_cnn_ray_tune pid=3216750)[0m 
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[36m(train_cnn_ray_tune pid=3216745)[0m 
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[36m(train_cnn_ray_tune pid=3216748)[0m 
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[36m(train_cnn_ray_tune pid=3216745)[0m 
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[36m(train_cnn_ray_tune pid=3216723)[0m 
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[36m(train_cnn_ray_tune pid=3216742)[0m 
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[36m(train_cnn_ray_tune pid=3216756)[0m 
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[36m(train_cnn_ray_tune pid=3216752)[0m 
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[36m(train_cnn_ray_tune pid=3216723)[0m 
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[36m(train_cnn_ray_tune pid=3216723)[0m Epoch 8/22[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=3216753)[0m 
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[36m(train_cnn_ray_tune pid=3216723)[0m 
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[36m(train_cnn_ray_tune pid=3216754)[0m 
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Trial status: 20 RUNNING
Current time: 2025-11-07 16:40:58. Total running time: 4min 0s
Logical resource usage: 20.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status       N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     num_resblocks     tasa_aprendizaje     epochs │
├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_987ea    RUNNING            2   adam            tanh                                   16                 32                  3                 0          1.76369e-05         22 │
│ trial_987ea    RUNNING            3   adam            tanh                                   16                 64                  3                 1          0.000124674         27 │
│ trial_987ea    RUNNING            3   rmsprop         tanh                                   32                 16                  3                 1          0.0001207           28 │
│ trial_987ea    RUNNING            2   rmsprop         tanh                                   16                 64                  3                 1          8.8649e-06          23 │
│ trial_987ea    RUNNING            2   adam            tanh                                   32                 16                  3                 1          0.000103558         26 │
│ trial_987ea    RUNNING            2   rmsprop         tanh                                   32                 16                  5                 0          0.000131282         20 │
│ trial_987ea    RUNNING            2   rmsprop         relu                                   32                 32                  5                 0          4.30906e-05         29 │
│ trial_987ea    RUNNING            3   rmsprop         relu                                   32                 32                  3                 0          6.42207e-06         25 │
│ trial_987ea    RUNNING            3   rmsprop         relu                                   16                 64                  5                 1          6.20968e-06         17 │
│ trial_987ea    RUNNING            2   rmsprop         tanh                                   32                 16                  3                 0          2.39155e-05         26 │
│ trial_987ea    RUNNING            3   rmsprop         tanh                                   32                 32                  3                 0          2.14512e-05         19 │
│ trial_987ea    RUNNING            3   adam            relu                                   16                 64                  5                 0          3.57777e-05         28 │
│ trial_987ea    RUNNING            2   adam            relu                                   32                 16                  3                 0          4.86659e-05         23 │
│ trial_987ea    RUNNING            3   rmsprop         tanh                                   16                 64                  3                 1          4.71571e-05         22 │
│ trial_987ea    RUNNING            3   adam            relu                                   16                 64                  5                 0          9.94182e-05         27 │
│ trial_987ea    RUNNING            2   adam            tanh                                   16                 16                  3                 0          0.000100097         16 │
│ trial_987ea    RUNNING            2   adam            relu                                   32                 64                  3                 1          1.89531e-05         28 │
│ trial_987ea    RUNNING            3   adam            tanh                                   32                 64                  5                 1          0.000197693         21 │
│ trial_987ea    RUNNING            3   rmsprop         relu                                   16                 64                  5                 1          0.000102887         19 │
│ trial_987ea    RUNNING            2   rmsprop         relu                                   16                 16                  3                 0          4.74263e-05         18 │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3216729)[0m 
[1m136/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m9s[0m 51ms/step - accuracy: 0.4919 - loss: 1.1450
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[36m(train_cnn_ray_tune pid=3216751)[0m 
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[36m(train_cnn_ray_tune pid=3216748)[0m 
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[36m(train_cnn_ray_tune pid=3216746)[0m 
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[36m(train_cnn_ray_tune pid=3216754)[0m 
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[36m(train_cnn_ray_tune pid=3216752)[0m 
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[36m(train_cnn_ray_tune pid=3216744)[0m 
[1m140/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m12s[0m 65ms/step - accuracy: 0.4659 - loss: 1.1636[32m [repeated 434x across cluster][0m
[36m(train_cnn_ray_tune pid=3216754)[0m 
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[36m(train_cnn_ray_tune pid=3216754)[0m Epoch 17/26
[36m(train_cnn_ray_tune pid=3216745)[0m 
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[36m(train_cnn_ray_tune pid=3216745)[0m Epoch 10/18
[36m(train_cnn_ray_tune pid=3216741)[0m 
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[36m(train_cnn_ray_tune pid=3216741)[0m 
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[36m(train_cnn_ray_tune pid=3216741)[0m 
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[36m(train_cnn_ray_tune pid=3216754)[0m 
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[1m105/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m8s[0m 40ms/step - accuracy: 0.4145 - loss: 1.2662[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3216748)[0m 
[1m 33/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m14s[0m 50ms/step - accuracy: 0.4691 - loss: 1.1704
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[36m(train_cnn_ray_tune pid=3216746)[0m 
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[36m(train_cnn_ray_tune pid=3216729)[0m 
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[36m(train_cnn_ray_tune pid=3216743)[0m 
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[36m(train_cnn_ray_tune pid=3216757)[0m 
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[36m(train_cnn_ray_tune pid=3216756)[0m 
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[36m(train_cnn_ray_tune pid=3216750)[0m 
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[36m(train_cnn_ray_tune pid=3216750)[0m Epoch 11/19[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=3216750)[0m 
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[36m(train_cnn_ray_tune pid=3216750)[0m 
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[36m(train_cnn_ray_tune pid=3216729)[0m 
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[36m(train_cnn_ray_tune pid=3216750)[0m 
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[36m(train_cnn_ray_tune pid=3216748)[0m 
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[36m(train_cnn_ray_tune pid=3216754)[0m 
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[36m(train_cnn_ray_tune pid=3216749)[0m 
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[36m(train_cnn_ray_tune pid=3216742)[0m 
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[36m(train_cnn_ray_tune pid=3216752)[0m 
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[36m(train_cnn_ray_tune pid=3216756)[0m 
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[36m(train_cnn_ray_tune pid=3216747)[0m 
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[36m(train_cnn_ray_tune pid=3216729)[0m 
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[36m(train_cnn_ray_tune pid=3216729)[0m Epoch 15/29[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3216753)[0m 
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[36m(train_cnn_ray_tune pid=3216729)[0m 
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[36m(train_cnn_ray_tune pid=3216744)[0m 
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[36m(train_cnn_ray_tune pid=3216749)[0m 
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[36m(train_cnn_ray_tune pid=3216747)[0m 
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[36m(train_cnn_ray_tune pid=3216745)[0m 
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[36m(train_cnn_ray_tune pid=3216757)[0m 
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[36m(train_cnn_ray_tune pid=3216750)[0m 
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[36m(train_cnn_ray_tune pid=3216741)[0m 
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[36m(train_cnn_ray_tune pid=3216749)[0m 
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[36m(train_cnn_ray_tune pid=3216743)[0m 
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[36m(train_cnn_ray_tune pid=3216743)[0m Epoch 8/23[32m [repeated 5x across cluster][0m
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[36m(train_cnn_ray_tune pid=3216754)[0m 
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[36m(train_cnn_ray_tune pid=3216755)[0m 
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[36m(train_cnn_ray_tune pid=3216755)[0m 
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[36m(train_cnn_ray_tune pid=3216755)[0m 
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[36m(train_cnn_ray_tune pid=3216755)[0m 
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[36m(train_cnn_ray_tune pid=3216729)[0m 
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[36m(train_cnn_ray_tune pid=3216751)[0m 
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[36m(train_cnn_ray_tune pid=3216755)[0m 
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[36m(train_cnn_ray_tune pid=3216755)[0m 
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[36m(train_cnn_ray_tune pid=3216752)[0m 
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[36m(train_cnn_ray_tune pid=3216749)[0m 
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[36m(train_cnn_ray_tune pid=3216744)[0m 
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[36m(train_cnn_ray_tune pid=3216755)[0m 
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[36m(train_cnn_ray_tune pid=3216755)[0m 
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[36m(train_cnn_ray_tune pid=3216748)[0m 
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[36m(train_cnn_ray_tune pid=3216723)[0m 
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[36m(train_cnn_ray_tune pid=3216755)[0m 
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[36m(train_cnn_ray_tune pid=3216755)[0m 
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[36m(train_cnn_ray_tune pid=3216755)[0m 
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[36m(train_cnn_ray_tune pid=3216755)[0m 
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[36m(train_cnn_ray_tune pid=3216755)[0m 
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[36m(train_cnn_ray_tune pid=3216748)[0m 
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[36m(train_cnn_ray_tune pid=3216748)[0m Epoch 17/20[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3216755)[0m 
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[36m(train_cnn_ray_tune pid=3216755)[0m 
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[36m(train_cnn_ray_tune pid=3216744)[0m 
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[36m(train_cnn_ray_tune pid=3216751)[0m 
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[36m(train_cnn_ray_tune pid=3216742)[0m 
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[36m(train_cnn_ray_tune pid=3216742)[0m 
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[36m(train_cnn_ray_tune pid=3216746)[0m 
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[36m(train_cnn_ray_tune pid=3216750)[0m 
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[36m(train_cnn_ray_tune pid=3216745)[0m 
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[36m(train_cnn_ray_tune pid=3216752)[0m 
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[36m(train_cnn_ray_tune pid=3216747)[0m 
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[36m(train_cnn_ray_tune pid=3216756)[0m 
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[36m(train_cnn_ray_tune pid=3216747)[0m 
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[36m(train_cnn_ray_tune pid=3216747)[0m Epoch 6/22[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3216729)[0m 
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[36m(train_cnn_ray_tune pid=3216751)[0m 
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[36m(train_cnn_ray_tune pid=3216745)[0m 
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Trial status: 20 RUNNING
Current time: 2025-11-07 16:41:28. Total running time: 4min 30s
Logical resource usage: 20.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status       N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     num_resblocks     tasa_aprendizaje     epochs │
├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_987ea    RUNNING            2   adam            tanh                                   16                 32                  3                 0          1.76369e-05         22 │
│ trial_987ea    RUNNING            3   adam            tanh                                   16                 64                  3                 1          0.000124674         27 │
│ trial_987ea    RUNNING            3   rmsprop         tanh                                   32                 16                  3                 1          0.0001207           28 │
│ trial_987ea    RUNNING            2   rmsprop         tanh                                   16                 64                  3                 1          8.8649e-06          23 │
│ trial_987ea    RUNNING            2   adam            tanh                                   32                 16                  3                 1          0.000103558         26 │
│ trial_987ea    RUNNING            2   rmsprop         tanh                                   32                 16                  5                 0          0.000131282         20 │
│ trial_987ea    RUNNING            2   rmsprop         relu                                   32                 32                  5                 0          4.30906e-05         29 │
│ trial_987ea    RUNNING            3   rmsprop         relu                                   32                 32                  3                 0          6.42207e-06         25 │
│ trial_987ea    RUNNING            3   rmsprop         relu                                   16                 64                  5                 1          6.20968e-06         17 │
│ trial_987ea    RUNNING            2   rmsprop         tanh                                   32                 16                  3                 0          2.39155e-05         26 │
│ trial_987ea    RUNNING            3   rmsprop         tanh                                   32                 32                  3                 0          2.14512e-05         19 │
│ trial_987ea    RUNNING            3   adam            relu                                   16                 64                  5                 0          3.57777e-05         28 │
│ trial_987ea    RUNNING            2   adam            relu                                   32                 16                  3                 0          4.86659e-05         23 │
│ trial_987ea    RUNNING            3   rmsprop         tanh                                   16                 64                  3                 1          4.71571e-05         22 │
│ trial_987ea    RUNNING            3   adam            relu                                   16                 64                  5                 0          9.94182e-05         27 │
│ trial_987ea    RUNNING            2   adam            tanh                                   16                 16                  3                 0          0.000100097         16 │
│ trial_987ea    RUNNING            2   adam            relu                                   32                 64                  3                 1          1.89531e-05         28 │
│ trial_987ea    RUNNING            3   adam            tanh                                   32                 64                  5                 1          0.000197693         21 │
│ trial_987ea    RUNNING            3   rmsprop         relu                                   16                 64                  5                 1          0.000102887         19 │
│ trial_987ea    RUNNING            2   rmsprop         relu                                   16                 16                  3                 0          4.74263e-05         18 │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3216754)[0m 
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[36m(train_cnn_ray_tune pid=3216745)[0m 
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[36m(train_cnn_ray_tune pid=3216756)[0m 
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[36m(train_cnn_ray_tune pid=3216756)[0m 
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[36m(train_cnn_ray_tune pid=3216749)[0m 
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[36m(train_cnn_ray_tune pid=3216723)[0m 
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[36m(train_cnn_ray_tune pid=3216752)[0m 
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[36m(train_cnn_ray_tune pid=3216741)[0m 
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[36m(train_cnn_ray_tune pid=3216750)[0m 
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[36m(train_cnn_ray_tune pid=3216750)[0m Epoch 12/19[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3216746)[0m 
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[36m(train_cnn_ray_tune pid=3216740)[0m 
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[36m(train_cnn_ray_tune pid=3216757)[0m 
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[36m(train_cnn_ray_tune pid=3216757)[0m 
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[36m(train_cnn_ray_tune pid=3216748)[0m 
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[36m(train_cnn_ray_tune pid=3216754)[0m 
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[36m(train_cnn_ray_tune pid=3216745)[0m 
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[36m(train_cnn_ray_tune pid=3216752)[0m 
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[36m(train_cnn_ray_tune pid=3216748)[0m 
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[36m(train_cnn_ray_tune pid=3216747)[0m 
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[36m(train_cnn_ray_tune pid=3216749)[0m 
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[36m(train_cnn_ray_tune pid=3216748)[0m 
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[36m(train_cnn_ray_tune pid=3216748)[0m Epoch 18/20[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3216748)[0m 
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[36m(train_cnn_ray_tune pid=3216751)[0m 
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[36m(train_cnn_ray_tune pid=3216741)[0m 
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[36m(train_cnn_ray_tune pid=3216749)[0m 
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[36m(train_cnn_ray_tune pid=3216753)[0m 
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[36m(train_cnn_ray_tune pid=3216752)[0m 
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[36m(train_cnn_ray_tune pid=3216741)[0m 
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[36m(train_cnn_ray_tune pid=3216756)[0m 
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[36m(train_cnn_ray_tune pid=3216744)[0m 
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[36m(train_cnn_ray_tune pid=3216747)[0m 
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[36m(train_cnn_ray_tune pid=3216751)[0m 
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[36m(train_cnn_ray_tune pid=3216756)[0m 
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[36m(train_cnn_ray_tune pid=3216756)[0m Epoch 6/19[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3216751)[0m 
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[36m(train_cnn_ray_tune pid=3216754)[0m 
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[36m(train_cnn_ray_tune pid=3216750)[0m 
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[36m(train_cnn_ray_tune pid=3216754)[0m 
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[36m(train_cnn_ray_tune pid=3216751)[0m 
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[36m(train_cnn_ray_tune pid=3216748)[0m 
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[36m(train_cnn_ray_tune pid=3216729)[0m 
[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m18s[0m 395ms/step
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[36m(train_cnn_ray_tune pid=3216729)[0m 
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[36m(train_cnn_ray_tune pid=3216729)[0m 
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[36m(train_cnn_ray_tune pid=3216729)[0m 
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[36m(train_cnn_ray_tune pid=3216729)[0m 
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[36m(train_cnn_ray_tune pid=3216745)[0m 
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[36m(train_cnn_ray_tune pid=3216729)[0m 
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[36m(train_cnn_ray_tune pid=3216729)[0m 
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[36m(train_cnn_ray_tune pid=3216729)[0m 
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[36m(train_cnn_ray_tune pid=3216745)[0m 
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[36m(train_cnn_ray_tune pid=3216747)[0m 
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[36m(train_cnn_ray_tune pid=3216729)[0m 
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[36m(train_cnn_ray_tune pid=3216729)[0m 
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[36m(train_cnn_ray_tune pid=3216729)[0m 
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[36m(train_cnn_ray_tune pid=3216723)[0m 
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[36m(train_cnn_ray_tune pid=3216729)[0m 
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[36m(train_cnn_ray_tune pid=3216729)[0m 
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[36m(train_cnn_ray_tune pid=3216754)[0m Epoch 20/26[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3216729)[0m 
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[36m(train_cnn_ray_tune pid=3216729)[0m 
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[36m(train_cnn_ray_tune pid=3216729)[0m 
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[36m(train_cnn_ray_tune pid=3216729)[0m 
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[36m(train_cnn_ray_tune pid=3216729)[0m 
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[36m(train_cnn_ray_tune pid=3216729)[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=3216729)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=3216755)[0m 2025-11-07 16:37:01.678987: 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=3216755)[0m 2025-11-07 16:37:01.699267: 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=3216755)[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=3216755)[0m E0000 00:00:1762529821.726797 3218041 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=3216755)[0m E0000 00:00:1762529821.734701 3218041 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=3216755)[0m W0000 00:00:1762529821.762860 3218041 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=3216755)[0m 2025-11-07 16:37:01.768697: 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=3216755)[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=3216755)[0m 2025-11-07 16:37:05.093441: 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=3216755)[0m 2025-11-07 16:37:05.093520: 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=3216755)[0m 2025-11-07 16:37:05.093532: 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=3216755)[0m 2025-11-07 16:37:05.093537: 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=3216755)[0m 2025-11-07 16:37:05.093542: 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=3216755)[0m 2025-11-07 16:37:05.093546: 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=3216755)[0m 2025-11-07 16:37:05.093928: 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=3216755)[0m 2025-11-07 16:37:05.093985: 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=3216755)[0m 2025-11-07 16:37:05.093990: 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=3216729)[0m 
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[36m(train_cnn_ray_tune pid=3216752)[0m 
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[36m(train_cnn_ray_tune pid=3216729)[0m 
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[36m(train_cnn_ray_tune pid=3216729)[0m 
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[36m(train_cnn_ray_tune pid=3216729)[0m 
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[36m(train_cnn_ray_tune pid=3216729)[0m 
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[36m(train_cnn_ray_tune pid=3216729)[0m 
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[36m(train_cnn_ray_tune pid=3216729)[0m 
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[36m(train_cnn_ray_tune pid=3216729)[0m 
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[36m(train_cnn_ray_tune pid=3216729)[0m 
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[36m(train_cnn_ray_tune pid=3216729)[0m 
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[36m(train_cnn_ray_tune pid=3216729)[0m 
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[36m(train_cnn_ray_tune pid=3216729)[0m 
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[36m(train_cnn_ray_tune pid=3216740)[0m 
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[36m(train_cnn_ray_tune pid=3216729)[0m 
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[36m(train_cnn_ray_tune pid=3216729)[0m 
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[36m(train_cnn_ray_tune pid=3216729)[0m 
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[36m(train_cnn_ray_tune pid=3216729)[0m 
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Trial trial_987ea finished iteration 1 at 2025-11-07 16:41:49. Total running time: 4min 51s
╭──────────────────────────────────────╮
│ Trial trial_987ea result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             288.299 │
│ time_total_s                 288.299 │
│ training_iteration                 1 │
│ val_accuracy                 0.50562 │
╰──────────────────────────────────────╯

Trial trial_987ea completed after 1 iterations at 2025-11-07 16:41:49. Total running time: 4min 51s
[36m(train_cnn_ray_tune pid=3216744)[0m 
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[36m(train_cnn_ray_tune pid=3216740)[0m 
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[36m(train_cnn_ray_tune pid=3216750)[0m 
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[36m(train_cnn_ray_tune pid=3216751)[0m 
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[36m(train_cnn_ray_tune pid=3216749)[0m 
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[36m(train_cnn_ray_tune pid=3216741)[0m 
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[36m(train_cnn_ray_tune pid=3216749)[0m 
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[36m(train_cnn_ray_tune pid=3216752)[0m 
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[36m(train_cnn_ray_tune pid=3216748)[0m 
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m16s[0m 48ms/step - accuracy: 0.4802 - loss: 1.1430 - val_accuracy: 0.5077 - val_loss: 1.1944[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3216748)[0m Epoch 19/20[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3216746)[0m 
[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m50s[0m 153ms/step - accuracy: 0.4062 - loss: 1.2297
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[36m(train_cnn_ray_tune pid=3216753)[0m 
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[36m(train_cnn_ray_tune pid=3216749)[0m 
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[36m(train_cnn_ray_tune pid=3216748)[0m 
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[36m(train_cnn_ray_tune pid=3216749)[0m 
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[36m(train_cnn_ray_tune pid=3216753)[0m 
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[36m(train_cnn_ray_tune pid=3216748)[0m 
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[36m(train_cnn_ray_tune pid=3216744)[0m 
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[36m(train_cnn_ray_tune pid=3216756)[0m 
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[36m(train_cnn_ray_tune pid=3216752)[0m 
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[36m(train_cnn_ray_tune pid=3216744)[0m 
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[36m(train_cnn_ray_tune pid=3216742)[0m 
[1m  2/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m28s[0m 87ms/step - accuracy: 0.5312 - loss: 0.9214  
[36m(train_cnn_ray_tune pid=3216742)[0m 
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[36m(train_cnn_ray_tune pid=3216742)[0m 
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m31s[0m 95ms/step - accuracy: 0.5382 - loss: 1.0495 - val_accuracy: 0.5179 - val_loss: 1.2117[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=3216742)[0m Epoch 10/21[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=3216751)[0m 
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[36m(train_cnn_ray_tune pid=3216742)[0m 
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[36m(train_cnn_ray_tune pid=3216745)[0m 
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Trial status: 19 RUNNING | 1 TERMINATED
Current time: 2025-11-07 16:41:58. Total running time: 5min 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     num_resblocks     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_987ea    RUNNING              2   adam            tanh                                   16                 32                  3                 0          1.76369e-05         22                                              │
│ trial_987ea    RUNNING              3   adam            tanh                                   16                 64                  3                 1          0.000124674         27                                              │
│ trial_987ea    RUNNING              3   rmsprop         tanh                                   32                 16                  3                 1          0.0001207           28                                              │
│ trial_987ea    RUNNING              2   rmsprop         tanh                                   16                 64                  3                 1          8.8649e-06          23                                              │
│ trial_987ea    RUNNING              2   adam            tanh                                   32                 16                  3                 1          0.000103558         26                                              │
│ trial_987ea    RUNNING              2   rmsprop         tanh                                   32                 16                  5                 0          0.000131282         20                                              │
│ trial_987ea    RUNNING              3   rmsprop         relu                                   32                 32                  3                 0          6.42207e-06         25                                              │
│ trial_987ea    RUNNING              3   rmsprop         relu                                   16                 64                  5                 1          6.20968e-06         17                                              │
│ trial_987ea    RUNNING              2   rmsprop         tanh                                   32                 16                  3                 0          2.39155e-05         26                                              │
│ trial_987ea    RUNNING              3   rmsprop         tanh                                   32                 32                  3                 0          2.14512e-05         19                                              │
│ trial_987ea    RUNNING              3   adam            relu                                   16                 64                  5                 0          3.57777e-05         28                                              │
│ trial_987ea    RUNNING              2   adam            relu                                   32                 16                  3                 0          4.86659e-05         23                                              │
│ trial_987ea    RUNNING              3   rmsprop         tanh                                   16                 64                  3                 1          4.71571e-05         22                                              │
│ trial_987ea    RUNNING              3   adam            relu                                   16                 64                  5                 0          9.94182e-05         27                                              │
│ trial_987ea    RUNNING              2   adam            tanh                                   16                 16                  3                 0          0.000100097         16                                              │
│ trial_987ea    RUNNING              2   adam            relu                                   32                 64                  3                 1          1.89531e-05         28                                              │
│ trial_987ea    RUNNING              3   adam            tanh                                   32                 64                  5                 1          0.000197693         21                                              │
│ trial_987ea    RUNNING              3   rmsprop         relu                                   16                 64                  5                 1          0.000102887         19                                              │
│ trial_987ea    RUNNING              2   rmsprop         relu                                   16                 16                  3                 0          4.74263e-05         18                                              │
│ trial_987ea    TERMINATED           2   rmsprop         relu                                   32                 32                  5                 0          4.30906e-05         29        1            288.299         0.505618 │
╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3216745)[0m 
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[36m(train_cnn_ray_tune pid=3216754)[0m 
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[36m(train_cnn_ray_tune pid=3216745)[0m 
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[36m(train_cnn_ray_tune pid=3216751)[0m 
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[36m(train_cnn_ray_tune pid=3216754)[0m 
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[36m(train_cnn_ray_tune pid=3216756)[0m 
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[36m(train_cnn_ray_tune pid=3216752)[0m 
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[36m(train_cnn_ray_tune pid=3216747)[0m 
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[36m(train_cnn_ray_tune pid=3216723)[0m 
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[36m(train_cnn_ray_tune pid=3216723)[0m Epoch 10/22[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3216723)[0m 
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[36m(train_cnn_ray_tune pid=3216749)[0m 
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[36m(train_cnn_ray_tune pid=3216745)[0m 
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[36m(train_cnn_ray_tune pid=3216755)[0m 
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[36m(train_cnn_ray_tune pid=3216749)[0m 
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[36m(train_cnn_ray_tune pid=3216749)[0m 
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[36m(train_cnn_ray_tune pid=3216756)[0m 
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[36m(train_cnn_ray_tune pid=3216752)[0m 
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[36m(train_cnn_ray_tune pid=3216755)[0m 
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[36m(train_cnn_ray_tune pid=3216749)[0m 
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[36m(train_cnn_ray_tune pid=3216749)[0m Epoch 20/26
[36m(train_cnn_ray_tune pid=3216748)[0m 
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[36m(train_cnn_ray_tune pid=3216748)[0m Epoch 20/20
[36m(train_cnn_ray_tune pid=3216748)[0m 
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[36m(train_cnn_ray_tune pid=3216754)[0m 
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[36m(train_cnn_ray_tune pid=3216753)[0m 
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[36m(train_cnn_ray_tune pid=3216748)[0m 
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[36m(train_cnn_ray_tune pid=3216755)[0m 
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[36m(train_cnn_ray_tune pid=3216750)[0m 
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[36m(train_cnn_ray_tune pid=3216745)[0m 
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[36m(train_cnn_ray_tune pid=3216742)[0m 
[36m(train_cnn_ray_tune pid=3216742)[0m 
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[36m(train_cnn_ray_tune pid=3216756)[0m 
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[36m(train_cnn_ray_tune pid=3216752)[0m 
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[36m(train_cnn_ray_tune pid=3216751)[0m 
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[36m(train_cnn_ray_tune pid=3216751)[0m Epoch 19/23[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3216751)[0m 
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[36m(train_cnn_ray_tune pid=3216754)[0m 
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[36m(train_cnn_ray_tune pid=3216754)[0m 
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[36m(train_cnn_ray_tune pid=3216744)[0m 
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[36m(train_cnn_ray_tune pid=3216745)[0m 
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[36m(train_cnn_ray_tune pid=3216751)[0m 
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[36m(train_cnn_ray_tune pid=3216748)[0m 
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[36m(train_cnn_ray_tune pid=3216741)[0m 
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[36m(train_cnn_ray_tune pid=3216752)[0m 
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[36m(train_cnn_ray_tune pid=3216745)[0m 
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[36m(train_cnn_ray_tune pid=3216750)[0m 
[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:01[0m 187ms/step - accuracy: 0.4375 - loss: 1.1344
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[36m(train_cnn_ray_tune pid=3216750)[0m 
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[36m(train_cnn_ray_tune pid=3216750)[0m Epoch 14/19[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3216744)[0m 
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[36m(train_cnn_ray_tune pid=3216747)[0m 
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[36m(train_cnn_ray_tune pid=3216749)[0m 
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[36m(train_cnn_ray_tune pid=3216746)[0m 
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[36m(train_cnn_ray_tune pid=3216755)[0m 
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[36m(train_cnn_ray_tune pid=3216753)[0m 
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[36m(train_cnn_ray_tune pid=3216755)[0m 
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[36m(train_cnn_ray_tune pid=3216755)[0m 
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[36m(train_cnn_ray_tune pid=3216756)[0m 
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[36m(train_cnn_ray_tune pid=3216751)[0m 
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[36m(train_cnn_ray_tune pid=3216755)[0m 
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[36m(train_cnn_ray_tune pid=3216755)[0m 
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[36m(train_cnn_ray_tune pid=3216755)[0m 
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[36m(train_cnn_ray_tune pid=3216745)[0m 
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[36m(train_cnn_ray_tune pid=3216752)[0m 
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[36m(train_cnn_ray_tune pid=3216755)[0m 
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[36m(train_cnn_ray_tune pid=3216755)[0m 
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[36m(train_cnn_ray_tune pid=3216749)[0m 
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[36m(train_cnn_ray_tune pid=3216749)[0m Epoch 21/26[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3216755)[0m 
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[36m(train_cnn_ray_tune pid=3216748)[0m 
[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m17s[0m 370ms/step
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[36m(train_cnn_ray_tune pid=3216748)[0m 
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[36m(train_cnn_ray_tune pid=3216748)[0m 
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[36m(train_cnn_ray_tune pid=3216748)[0m 
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[36m(train_cnn_ray_tune pid=3216748)[0m 
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[36m(train_cnn_ray_tune pid=3216748)[0m 
[1m33/49[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 17ms/step
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[36m(train_cnn_ray_tune pid=3216723)[0m 
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[36m(train_cnn_ray_tune pid=3216748)[0m 
[1m38/49[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 18ms/step
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[36m(train_cnn_ray_tune pid=3216748)[0m 
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[36m(train_cnn_ray_tune pid=3216748)[0m 
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 25ms/step
[36m(train_cnn_ray_tune pid=3216748)[0m 
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 25ms/step
[36m(train_cnn_ray_tune pid=3216748)[0m 
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[36m(train_cnn_ray_tune pid=3216748)[0m 
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[36m(train_cnn_ray_tune pid=3216748)[0m 
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[36m(train_cnn_ray_tune pid=3216748)[0m 
[1m28/89[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 16ms/step
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[36m(train_cnn_ray_tune pid=3216756)[0m 
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[36m(train_cnn_ray_tune pid=3216748)[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=3216748)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=3216749)[0m 
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[36m(train_cnn_ray_tune pid=3216748)[0m 
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[36m(train_cnn_ray_tune pid=3216748)[0m 
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[36m(train_cnn_ray_tune pid=3216748)[0m 
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[36m(train_cnn_ray_tune pid=3216748)[0m 
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[36m(train_cnn_ray_tune pid=3216748)[0m 
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[36m(train_cnn_ray_tune pid=3216755)[0m 
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[36m(train_cnn_ray_tune pid=3216752)[0m 
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[36m(train_cnn_ray_tune pid=3216748)[0m 
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[36m(train_cnn_ray_tune pid=3216748)[0m 
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[36m(train_cnn_ray_tune pid=3216748)[0m 
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Trial trial_987ea finished iteration 1 at 2025-11-07 16:42:26. Total running time: 5min 28s
╭──────────────────────────────────────╮
│ Trial trial_987ea result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             325.689 │
│ time_total_s                 325.689 │
│ training_iteration                 1 │
│ val_accuracy                  0.5144 │
╰──────────────────────────────────────╯

Trial trial_987ea completed after 1 iterations at 2025-11-07 16:42:26. Total running time: 5min 28s
[36m(train_cnn_ray_tune pid=3216743)[0m 
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[36m(train_cnn_ray_tune pid=3216742)[0m 
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[36m(train_cnn_ray_tune pid=3216742)[0m 
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[36m(train_cnn_ray_tune pid=3216742)[0m Epoch 11/21[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=3216750)[0m 
[1m169/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m9s[0m 63ms/step - accuracy: 0.4143 - loss: 1.3134 
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[36m(train_cnn_ray_tune pid=3216754)[0m 
[1m  3/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 36ms/step - accuracy: 0.5139 - loss: 1.0648 [32m [repeated 2x across cluster][0m

Trial status: 18 RUNNING | 2 TERMINATED
Current time: 2025-11-07 16:42:28. Total running time: 5min 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     num_resblocks     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_987ea    RUNNING              2   adam            tanh                                   16                 32                  3                 0          1.76369e-05         22                                              │
│ trial_987ea    RUNNING              3   adam            tanh                                   16                 64                  3                 1          0.000124674         27                                              │
│ trial_987ea    RUNNING              3   rmsprop         tanh                                   32                 16                  3                 1          0.0001207           28                                              │
│ trial_987ea    RUNNING              2   rmsprop         tanh                                   16                 64                  3                 1          8.8649e-06          23                                              │
│ trial_987ea    RUNNING              2   adam            tanh                                   32                 16                  3                 1          0.000103558         26                                              │
│ trial_987ea    RUNNING              3   rmsprop         relu                                   32                 32                  3                 0          6.42207e-06         25                                              │
│ trial_987ea    RUNNING              3   rmsprop         relu                                   16                 64                  5                 1          6.20968e-06         17                                              │
│ trial_987ea    RUNNING              2   rmsprop         tanh                                   32                 16                  3                 0          2.39155e-05         26                                              │
│ trial_987ea    RUNNING              3   rmsprop         tanh                                   32                 32                  3                 0          2.14512e-05         19                                              │
│ trial_987ea    RUNNING              3   adam            relu                                   16                 64                  5                 0          3.57777e-05         28                                              │
│ trial_987ea    RUNNING              2   adam            relu                                   32                 16                  3                 0          4.86659e-05         23                                              │
│ trial_987ea    RUNNING              3   rmsprop         tanh                                   16                 64                  3                 1          4.71571e-05         22                                              │
│ trial_987ea    RUNNING              3   adam            relu                                   16                 64                  5                 0          9.94182e-05         27                                              │
│ trial_987ea    RUNNING              2   adam            tanh                                   16                 16                  3                 0          0.000100097         16                                              │
│ trial_987ea    RUNNING              2   adam            relu                                   32                 64                  3                 1          1.89531e-05         28                                              │
│ trial_987ea    RUNNING              3   adam            tanh                                   32                 64                  5                 1          0.000197693         21                                              │
│ trial_987ea    RUNNING              3   rmsprop         relu                                   16                 64                  5                 1          0.000102887         19                                              │
│ trial_987ea    RUNNING              2   rmsprop         relu                                   16                 16                  3                 0          4.74263e-05         18                                              │
│ trial_987ea    TERMINATED           2   rmsprop         tanh                                   32                 16                  5                 0          0.000131282         20        1            325.689         0.514396 │
│ trial_987ea    TERMINATED           2   rmsprop         relu                                   32                 32                  5                 0          4.30906e-05         29        1            288.299         0.505618 │
╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3216754)[0m 
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[36m(train_cnn_ray_tune pid=3216752)[0m 
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[36m(train_cnn_ray_tune pid=3216745)[0m 
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[36m(train_cnn_ray_tune pid=3216742)[0m 
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[36m(train_cnn_ray_tune pid=3216742)[0m 
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[36m(train_cnn_ray_tune pid=3216755)[0m 
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[36m(train_cnn_ray_tune pid=3216757)[0m 
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[36m(train_cnn_ray_tune pid=3216757)[0m 
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[36m(train_cnn_ray_tune pid=3216746)[0m 
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[36m(train_cnn_ray_tune pid=3216746)[0m Epoch 17/28[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3216754)[0m 
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[36m(train_cnn_ray_tune pid=3216746)[0m 
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[36m(train_cnn_ray_tune pid=3216723)[0m 
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[36m(train_cnn_ray_tune pid=3216745)[0m 
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[36m(train_cnn_ray_tune pid=3216751)[0m 
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[36m(train_cnn_ray_tune pid=3216750)[0m 
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[36m(train_cnn_ray_tune pid=3216749)[0m 
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[36m(train_cnn_ray_tune pid=3216749)[0m 
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[36m(train_cnn_ray_tune pid=3216743)[0m 
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[36m(train_cnn_ray_tune pid=3216746)[0m 
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[36m(train_cnn_ray_tune pid=3216744)[0m 
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[36m(train_cnn_ray_tune pid=3216751)[0m 
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[36m(train_cnn_ray_tune pid=3216753)[0m 
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[36m(train_cnn_ray_tune pid=3216741)[0m 
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[36m(train_cnn_ray_tune pid=3216741)[0m Epoch 14/25[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=3216745)[0m 
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[36m(train_cnn_ray_tune pid=3216741)[0m 
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[36m(train_cnn_ray_tune pid=3216749)[0m 
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[36m(train_cnn_ray_tune pid=3216749)[0m 
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[36m(train_cnn_ray_tune pid=3216741)[0m 
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[36m(train_cnn_ray_tune pid=3216754)[0m 
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[36m(train_cnn_ray_tune pid=3216751)[0m 
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[36m(train_cnn_ray_tune pid=3216752)[0m 
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[36m(train_cnn_ray_tune pid=3216756)[0m 
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[36m(train_cnn_ray_tune pid=3216752)[0m 
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[36m(train_cnn_ray_tune pid=3216751)[0m 
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[36m(train_cnn_ray_tune pid=3216740)[0m 
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[36m(train_cnn_ray_tune pid=3216746)[0m 
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[36m(train_cnn_ray_tune pid=3216740)[0m 
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[36m(train_cnn_ray_tune pid=3216740)[0m Epoch 7/27[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3216745)[0m 
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[36m(train_cnn_ray_tune pid=3216742)[0m 
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[36m(train_cnn_ray_tune pid=3216745)[0m 
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[36m(train_cnn_ray_tune pid=3216740)[0m 
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[36m(train_cnn_ray_tune pid=3216756)[0m 
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[36m(train_cnn_ray_tune pid=3216752)[0m 
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[36m(train_cnn_ray_tune pid=3216753)[0m 
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[36m(train_cnn_ray_tune pid=3216751)[0m 
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[36m(train_cnn_ray_tune pid=3216754)[0m 
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[36m(train_cnn_ray_tune pid=3216749)[0m 
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[36m(train_cnn_ray_tune pid=3216749)[0m Epoch 23/26
[36m(train_cnn_ray_tune pid=3216749)[0m 
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[36m(train_cnn_ray_tune pid=3216749)[0m 
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[36m(train_cnn_ray_tune pid=3216744)[0m 
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[36m(train_cnn_ray_tune pid=3216741)[0m 
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[36m(train_cnn_ray_tune pid=3216753)[0m 
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[36m(train_cnn_ray_tune pid=3216752)[0m 
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[36m(train_cnn_ray_tune pid=3216723)[0m 
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[36m(train_cnn_ray_tune pid=3216756)[0m 
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[36m(train_cnn_ray_tune pid=3216756)[0m 
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[36m(train_cnn_ray_tune pid=3216751)[0m 
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[36m(train_cnn_ray_tune pid=3216751)[0m Epoch 22/23[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3216751)[0m 
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[36m(train_cnn_ray_tune pid=3216751)[0m 
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[36m(train_cnn_ray_tune pid=3216754)[0m 
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[36m(train_cnn_ray_tune pid=3216745)[0m 
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[36m(train_cnn_ray_tune pid=3216723)[0m 
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[36m(train_cnn_ray_tune pid=3216756)[0m 
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[36m(train_cnn_ray_tune pid=3216752)[0m 
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[36m(train_cnn_ray_tune pid=3216751)[0m 
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[36m(train_cnn_ray_tune pid=3216742)[0m 
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[36m(train_cnn_ray_tune pid=3216742)[0m 
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[36m(train_cnn_ray_tune pid=3216742)[0m 
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[36m(train_cnn_ray_tune pid=3216742)[0m 
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[36m(train_cnn_ray_tune pid=3216742)[0m 
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[36m(train_cnn_ray_tune pid=3216744)[0m 
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[36m(train_cnn_ray_tune pid=3216742)[0m 
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[36m(train_cnn_ray_tune pid=3216742)[0m 
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[36m(train_cnn_ray_tune pid=3216751)[0m 
[1m 62/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m9s[0m 37ms/step - accuracy: 0.4732 - loss: 1.1763 
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[36m(train_cnn_ray_tune pid=3216742)[0m 
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[36m(train_cnn_ray_tune pid=3216742)[0m 
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[36m(train_cnn_ray_tune pid=3216742)[0m 
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[36m(train_cnn_ray_tune pid=3216742)[0m 
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[36m(train_cnn_ray_tune pid=3216742)[0m 
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[36m(train_cnn_ray_tune pid=3216742)[0m 
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[36m(train_cnn_ray_tune pid=3216750)[0m 
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Trial status: 18 RUNNING | 2 TERMINATED
Current time: 2025-11-07 16:42:58. Total running time: 6min 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     num_resblocks     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_987ea    RUNNING              2   adam            tanh                                   16                 32                  3                 0          1.76369e-05         22                                              │
│ trial_987ea    RUNNING              3   adam            tanh                                   16                 64                  3                 1          0.000124674         27                                              │
│ trial_987ea    RUNNING              3   rmsprop         tanh                                   32                 16                  3                 1          0.0001207           28                                              │
│ trial_987ea    RUNNING              2   rmsprop         tanh                                   16                 64                  3                 1          8.8649e-06          23                                              │
│ trial_987ea    RUNNING              2   adam            tanh                                   32                 16                  3                 1          0.000103558         26                                              │
│ trial_987ea    RUNNING              3   rmsprop         relu                                   32                 32                  3                 0          6.42207e-06         25                                              │
│ trial_987ea    RUNNING              3   rmsprop         relu                                   16                 64                  5                 1          6.20968e-06         17                                              │
│ trial_987ea    RUNNING              2   rmsprop         tanh                                   32                 16                  3                 0          2.39155e-05         26                                              │
│ trial_987ea    RUNNING              3   rmsprop         tanh                                   32                 32                  3                 0          2.14512e-05         19                                              │
│ trial_987ea    RUNNING              3   adam            relu                                   16                 64                  5                 0          3.57777e-05         28                                              │
│ trial_987ea    RUNNING              2   adam            relu                                   32                 16                  3                 0          4.86659e-05         23                                              │
│ trial_987ea    RUNNING              3   rmsprop         tanh                                   16                 64                  3                 1          4.71571e-05         22                                              │
│ trial_987ea    RUNNING              3   adam            relu                                   16                 64                  5                 0          9.94182e-05         27                                              │
│ trial_987ea    RUNNING              2   adam            tanh                                   16                 16                  3                 0          0.000100097         16                                              │
│ trial_987ea    RUNNING              2   adam            relu                                   32                 64                  3                 1          1.89531e-05         28                                              │
│ trial_987ea    RUNNING              3   adam            tanh                                   32                 64                  5                 1          0.000197693         21                                              │
│ trial_987ea    RUNNING              3   rmsprop         relu                                   16                 64                  5                 1          0.000102887         19                                              │
│ trial_987ea    RUNNING              2   rmsprop         relu                                   16                 16                  3                 0          4.74263e-05         18                                              │
│ trial_987ea    TERMINATED           2   rmsprop         tanh                                   32                 16                  5                 0          0.000131282         20        1            325.689         0.514396 │
│ trial_987ea    TERMINATED           2   rmsprop         relu                                   32                 32                  5                 0          4.30906e-05         29        1            288.299         0.505618 │
╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
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[36m(train_cnn_ray_tune pid=3216745)[0m Epoch 14/18[32m [repeated 3x across cluster][0m
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[36m(train_cnn_ray_tune pid=3216741)[0m 
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[36m(train_cnn_ray_tune pid=3216742)[0m 
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[36m(train_cnn_ray_tune pid=3216751)[0m 
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[36m(train_cnn_ray_tune pid=3216755)[0m 
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[36m(train_cnn_ray_tune pid=3216742)[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=3216742)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=3216742)[0m 
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[36m(train_cnn_ray_tune pid=3216752)[0m 
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[36m(train_cnn_ray_tune pid=3216741)[0m 
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[36m(train_cnn_ray_tune pid=3216742)[0m 
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[36m(train_cnn_ray_tune pid=3216742)[0m 
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[36m(train_cnn_ray_tune pid=3216742)[0m 
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[36m(train_cnn_ray_tune pid=3216742)[0m 
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[36m(train_cnn_ray_tune pid=3216742)[0m 
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Trial trial_987ea finished iteration 1 at 2025-11-07 16:43:02. Total running time: 6min 4s
╭──────────────────────────────────────╮
│ Trial trial_987ea result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             361.388 │
│ time_total_s                 361.388 │
│ training_iteration                 1 │
│ val_accuracy                  0.4842 │
╰──────────────────────────────────────╯

Trial trial_987ea completed after 1 iterations at 2025-11-07 16:43:02. Total running time: 6min 4s
[36m(train_cnn_ray_tune pid=3216751)[0m 
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[36m(train_cnn_ray_tune pid=3216755)[0m 
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[36m(train_cnn_ray_tune pid=3216753)[0m 
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[36m(train_cnn_ray_tune pid=3216749)[0m 
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[36m(train_cnn_ray_tune pid=3216749)[0m Epoch 24/26[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=3216750)[0m 
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[36m(train_cnn_ray_tune pid=3216723)[0m 
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[36m(train_cnn_ray_tune pid=3216723)[0m 
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[36m(train_cnn_ray_tune pid=3216751)[0m 
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[36m(train_cnn_ray_tune pid=3216744)[0m 
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[36m(train_cnn_ray_tune pid=3216752)[0m 
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[36m(train_cnn_ray_tune pid=3216745)[0m 
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[36m(train_cnn_ray_tune pid=3216750)[0m 
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[36m(train_cnn_ray_tune pid=3216747)[0m 
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[36m(train_cnn_ray_tune pid=3216754)[0m 
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[36m(train_cnn_ray_tune pid=3216746)[0m 
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[36m(train_cnn_ray_tune pid=3216746)[0m Epoch 19/28[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3216746)[0m 
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[36m(train_cnn_ray_tune pid=3216746)[0m 
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[36m(train_cnn_ray_tune pid=3216756)[0m 
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[36m(train_cnn_ray_tune pid=3216749)[0m 
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[36m(train_cnn_ray_tune pid=3216745)[0m 
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[36m(train_cnn_ray_tune pid=3216752)[0m 
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[36m(train_cnn_ray_tune pid=3216743)[0m 
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[36m(train_cnn_ray_tune pid=3216754)[0m 
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[36m(train_cnn_ray_tune pid=3216753)[0m 
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[36m(train_cnn_ray_tune pid=3216744)[0m 
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[36m(train_cnn_ray_tune pid=3216755)[0m 
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[36m(train_cnn_ray_tune pid=3216755)[0m Epoch 7/28[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3216755)[0m 
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[36m(train_cnn_ray_tune pid=3216752)[0m 
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[36m(train_cnn_ray_tune pid=3216754)[0m 
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[36m(train_cnn_ray_tune pid=3216746)[0m 
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[36m(train_cnn_ray_tune pid=3216755)[0m 
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[36m(train_cnn_ray_tune pid=3216751)[0m 
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[36m(train_cnn_ray_tune pid=3216749)[0m 
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[36m(train_cnn_ray_tune pid=3216749)[0m 
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[36m(train_cnn_ray_tune pid=3216755)[0m 
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[36m(train_cnn_ray_tune pid=3216750)[0m 
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[36m(train_cnn_ray_tune pid=3216750)[0m 
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[36m(train_cnn_ray_tune pid=3216750)[0m Epoch 16/19[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3216750)[0m 
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[36m(train_cnn_ray_tune pid=3216752)[0m 
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[36m(train_cnn_ray_tune pid=3216751)[0m 
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[36m(train_cnn_ray_tune pid=3216745)[0m 
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[36m(train_cnn_ray_tune pid=3216754)[0m 
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[36m(train_cnn_ray_tune pid=3216754)[0m 
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[36m(train_cnn_ray_tune pid=3216754)[0m 
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[36m(train_cnn_ray_tune pid=3216754)[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=3216754)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=3216754)[0m 
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Trial trial_987ea finished iteration 1 at 2025-11-07 16:43:23. Total running time: 6min 26s
╭──────────────────────────────────────╮
│ Trial trial_987ea result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             383.124 │
│ time_total_s                 383.124 │
│ training_iteration                 1 │
│ val_accuracy                  0.4849 │
╰──────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3216754)[0m 
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Trial trial_987ea completed after 1 iterations at 2025-11-07 16:43:23. Total running time: 6min 26s
[36m(train_cnn_ray_tune pid=3216757)[0m 
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[36m(train_cnn_ray_tune pid=3216757)[0m Epoch 7/27[32m [repeated 4x across cluster][0m
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[36m(train_cnn_ray_tune pid=3216751)[0m 
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[36m(train_cnn_ray_tune pid=3216750)[0m 
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[36m(train_cnn_ray_tune pid=3216746)[0m 
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[36m(train_cnn_ray_tune pid=3216753)[0m 
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[36m(train_cnn_ray_tune pid=3216756)[0m 
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[36m(train_cnn_ray_tune pid=3216741)[0m 
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[36m(train_cnn_ray_tune pid=3216751)[0m 
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[36m(train_cnn_ray_tune pid=3216756)[0m 
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[36m(train_cnn_ray_tune pid=3216751)[0m 
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Trial status: 16 RUNNING | 4 TERMINATED
Current time: 2025-11-07 16:43:28. Total running time: 6min 30s
Logical resource usage: 16.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     num_resblocks     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_987ea    RUNNING              2   adam            tanh                                   16                 32                  3                 0          1.76369e-05         22                                              │
│ trial_987ea    RUNNING              3   adam            tanh                                   16                 64                  3                 1          0.000124674         27                                              │
│ trial_987ea    RUNNING              3   rmsprop         tanh                                   32                 16                  3                 1          0.0001207           28                                              │
│ trial_987ea    RUNNING              2   rmsprop         tanh                                   16                 64                  3                 1          8.8649e-06          23                                              │
│ trial_987ea    RUNNING              2   adam            tanh                                   32                 16                  3                 1          0.000103558         26                                              │
│ trial_987ea    RUNNING              3   rmsprop         relu                                   32                 32                  3                 0          6.42207e-06         25                                              │
│ trial_987ea    RUNNING              3   rmsprop         relu                                   16                 64                  5                 1          6.20968e-06         17                                              │
│ trial_987ea    RUNNING              3   rmsprop         tanh                                   32                 32                  3                 0          2.14512e-05         19                                              │
│ trial_987ea    RUNNING              3   adam            relu                                   16                 64                  5                 0          3.57777e-05         28                                              │
│ trial_987ea    RUNNING              2   adam            relu                                   32                 16                  3                 0          4.86659e-05         23                                              │
│ trial_987ea    RUNNING              3   rmsprop         tanh                                   16                 64                  3                 1          4.71571e-05         22                                              │
│ trial_987ea    RUNNING              3   adam            relu                                   16                 64                  5                 0          9.94182e-05         27                                              │
│ trial_987ea    RUNNING              2   adam            tanh                                   16                 16                  3                 0          0.000100097         16                                              │
│ trial_987ea    RUNNING              2   adam            relu                                   32                 64                  3                 1          1.89531e-05         28                                              │
│ trial_987ea    RUNNING              3   rmsprop         relu                                   16                 64                  5                 1          0.000102887         19                                              │
│ trial_987ea    RUNNING              2   rmsprop         relu                                   16                 16                  3                 0          4.74263e-05         18                                              │
│ trial_987ea    TERMINATED           2   rmsprop         tanh                                   32                 16                  5                 0          0.000131282         20        1            325.689         0.514396 │
│ trial_987ea    TERMINATED           2   rmsprop         relu                                   32                 32                  5                 0          4.30906e-05         29        1            288.299         0.505618 │
│ trial_987ea    TERMINATED           2   rmsprop         tanh                                   32                 16                  3                 0          2.39155e-05         26        1            383.124         0.484902 │
│ trial_987ea    TERMINATED           3   adam            tanh                                   32                 64                  5                 1          0.000197693         21        1            361.388         0.484199 │
╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3216751)[0m 
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[1m18/49[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 13ms/step
[36m(train_cnn_ray_tune pid=3216751)[0m 
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[36m(train_cnn_ray_tune pid=3216751)[0m 
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[36m(train_cnn_ray_tune pid=3216751)[0m 
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[1m42/49[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 15ms/step
[36m(train_cnn_ray_tune pid=3216751)[0m 
[1m48/49[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 14ms/step
[36m(train_cnn_ray_tune pid=3216750)[0m 
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[36m(train_cnn_ray_tune pid=3216743)[0m 
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[36m(train_cnn_ray_tune pid=3216751)[0m 
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 23ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 24ms/step
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[36m(train_cnn_ray_tune pid=3216750)[0m 
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[36m(train_cnn_ray_tune pid=3216751)[0m 
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[36m(train_cnn_ray_tune pid=3216751)[0m 
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[36m(train_cnn_ray_tune pid=3216749)[0m 
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[36m(train_cnn_ray_tune pid=3216751)[0m 
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[36m(train_cnn_ray_tune pid=3216751)[0m 
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[36m(train_cnn_ray_tune pid=3216744)[0m 
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[36m(train_cnn_ray_tune pid=3216743)[0m Epoch 12/23[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=3216751)[0m 
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[36m(train_cnn_ray_tune pid=3216749)[0m 
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[36m(train_cnn_ray_tune pid=3216745)[0m 
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[36m(train_cnn_ray_tune pid=3216749)[0m 
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[36m(train_cnn_ray_tune pid=3216751)[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=3216751)[0m   _log_deprecation_warning(
Trial trial_987ea finished iteration 1 at 2025-11-07 16:43:31. Total running time: 6min 33s
╭──────────────────────────────────────╮
│ Trial trial_987ea result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             390.349 │
│ time_total_s                 390.349 │
│ training_iteration                 1 │
│ val_accuracy                 0.51721 │
╰──────────────────────────────────────╯

Trial trial_987ea completed after 1 iterations at 2025-11-07 16:43:31. Total running time: 6min 33s
[36m(train_cnn_ray_tune pid=3216743)[0m 
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[36m(train_cnn_ray_tune pid=3216740)[0m 
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[36m(train_cnn_ray_tune pid=3216752)[0m 
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[36m(train_cnn_ray_tune pid=3216753)[0m 
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[36m(train_cnn_ray_tune pid=3216744)[0m 
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[36m(train_cnn_ray_tune pid=3216750)[0m 
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[36m(train_cnn_ray_tune pid=3216756)[0m 
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[36m(train_cnn_ray_tune pid=3216744)[0m 
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[36m(train_cnn_ray_tune pid=3216744)[0m 
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Trial trial_987ea finished iteration 1 at 2025-11-07 16:43:34. Total running time: 6min 36s
╭──────────────────────────────────────╮
│ Trial trial_987ea result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             393.491 │
│ time_total_s                 393.491 │
│ training_iteration                 1 │
│ val_accuracy                 0.49754 │
╰──────────────────────────────────────╯

Trial trial_987ea completed after 1 iterations at 2025-11-07 16:43:34. Total running time: 6min 36s
[36m(train_cnn_ray_tune pid=3216745)[0m 
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[36m(train_cnn_ray_tune pid=3216723)[0m 
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[36m(train_cnn_ray_tune pid=3216723)[0m 
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[36m(train_cnn_ray_tune pid=3216745)[0m 
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[36m(train_cnn_ray_tune pid=3216747)[0m 
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Trial trial_987ea finished iteration 1 at 2025-11-07 16:43:36. Total running time: 6min 38s
[36m(train_cnn_ray_tune pid=3216723)[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=3216723)[0m   _log_deprecation_warning([32m [repeated 2x across cluster][0m
╭──────────────────────────────────────╮
│ Trial trial_987ea result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             395.874 │
│ time_total_s                 395.874 │
│ training_iteration                 1 │
│ val_accuracy                 0.47331 │
╰──────────────────────────────────────╯

Trial trial_987ea completed after 1 iterations at 2025-11-07 16:43:36. Total running time: 6min 38s
[36m(train_cnn_ray_tune pid=3216756)[0m 
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[36m(train_cnn_ray_tune pid=3216752)[0m 
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[36m(train_cnn_ray_tune pid=3216750)[0m 
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[36m(train_cnn_ray_tune pid=3216750)[0m Epoch 17/19
[36m(train_cnn_ray_tune pid=3216750)[0m 
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[36m(train_cnn_ray_tune pid=3216750)[0m 
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[36m(train_cnn_ray_tune pid=3216723)[0m 
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[36m(train_cnn_ray_tune pid=3216741)[0m 
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[36m(train_cnn_ray_tune pid=3216753)[0m 
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[36m(train_cnn_ray_tune pid=3216746)[0m 
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[36m(train_cnn_ray_tune pid=3216752)[0m 
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[36m(train_cnn_ray_tune pid=3216743)[0m 
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[36m(train_cnn_ray_tune pid=3216749)[0m 
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[36m(train_cnn_ray_tune pid=3216749)[0m 
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[36m(train_cnn_ray_tune pid=3216741)[0m Epoch 17/25
[36m(train_cnn_ray_tune pid=3216753)[0m 
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[36m(train_cnn_ray_tune pid=3216741)[0m 
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[36m(train_cnn_ray_tune pid=3216746)[0m Epoch 21/28
[36m(train_cnn_ray_tune pid=3216746)[0m 
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[36m(train_cnn_ray_tune pid=3216746)[0m 
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[36m(train_cnn_ray_tune pid=3216745)[0m 
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[36m(train_cnn_ray_tune pid=3216749)[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=3216749)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=3216749)[0m 
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[36m(train_cnn_ray_tune pid=3216749)[0m 
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[36m(train_cnn_ray_tune pid=3216743)[0m 
[1m401/655[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m9s[0m 39ms/step - accuracy: 0.4030 - loss: 1.3629 
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Trial trial_987ea finished iteration 1 at 2025-11-07 16:43:45. Total running time: 6min 47s
╭──────────────────────────────────────╮
│ Trial trial_987ea result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             404.185 │
│ time_total_s                 404.185 │
│ training_iteration                 1 │
│ val_accuracy                 0.50667 │
╰──────────────────────────────────────╯

Trial trial_987ea completed after 1 iterations at 2025-11-07 16:43:45. Total running time: 6min 47s
[36m(train_cnn_ray_tune pid=3216750)[0m 
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[36m(train_cnn_ray_tune pid=3216743)[0m 
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[36m(train_cnn_ray_tune pid=3216745)[0m 
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[36m(train_cnn_ray_tune pid=3216752)[0m 
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[36m(train_cnn_ray_tune pid=3216745)[0m 
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[36m(train_cnn_ray_tune pid=3216755)[0m 
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[36m(train_cnn_ray_tune pid=3216747)[0m 
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[36m(train_cnn_ray_tune pid=3216747)[0m Epoch 9/22[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3216747)[0m 
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[36m(train_cnn_ray_tune pid=3216749)[0m 
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[36m(train_cnn_ray_tune pid=3216746)[0m 
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[36m(train_cnn_ray_tune pid=3216741)[0m 
[1m209/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m5s[0m 49ms/step - accuracy: 0.4159 - loss: 1.3997
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[36m(train_cnn_ray_tune pid=3216746)[0m 
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[36m(train_cnn_ray_tune pid=3216755)[0m 
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[36m(train_cnn_ray_tune pid=3216753)[0m 
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[36m(train_cnn_ray_tune pid=3216740)[0m 
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[36m(train_cnn_ray_tune pid=3216753)[0m 
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[36m(train_cnn_ray_tune pid=3216745)[0m 
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[36m(train_cnn_ray_tune pid=3216753)[0m 
[1m108/655[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m17s[0m 31ms/step - accuracy: 0.4344 - loss: 1.1766
[1m110/655[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m16s[0m 31ms/step - accuracy: 0.4345 - loss: 1.1769
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[36m(train_cnn_ray_tune pid=3216753)[0m Epoch 15/16
[36m(train_cnn_ray_tune pid=3216757)[0m 
[1m493/655[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m9s[0m 62ms/step - accuracy: 0.5079 - loss: 1.1152 
[1m494/655[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m9s[0m 62ms/step - accuracy: 0.5079 - loss: 1.1152[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3216753)[0m 
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[1m192/655[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m14s[0m 31ms/step - accuracy: 0.4365 - loss: 1.1862
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[36m(train_cnn_ray_tune pid=3216750)[0m 
[1m271/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m2s[0m 49ms/step - accuracy: 0.4338 - loss: 1.2717
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[1m273/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m2s[0m 49ms/step - accuracy: 0.4338 - loss: 1.2717
[36m(train_cnn_ray_tune pid=3216750)[0m Epoch 18/19
[36m(train_cnn_ray_tune pid=3216745)[0m 
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[36m(train_cnn_ray_tune pid=3216746)[0m 
[1m313/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 42ms/step - accuracy: 0.5087 - loss: 1.1296[32m [repeated 167x across cluster][0m
[36m(train_cnn_ray_tune pid=3216750)[0m 
[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m39s[0m 120ms/step - accuracy: 0.4062 - loss: 1.2174
[1m  3/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 43ms/step - accuracy: 0.4219 - loss: 1.2486 
[36m(train_cnn_ray_tune pid=3216745)[0m 
[1m483/655[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m4s[0m 28ms/step - accuracy: 0.4555 - loss: 1.2091
[1m485/655[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m4s[0m 28ms/step - accuracy: 0.4554 - loss: 1.2091
[1m488/655[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m4s[0m 28ms/step - accuracy: 0.4554 - loss: 1.2091
[36m(train_cnn_ray_tune pid=3216750)[0m 
[1m 23/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 39ms/step - accuracy: 0.4099 - loss: 1.2801
[1m 24/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 39ms/step - accuracy: 0.4094 - loss: 1.2808[32m [repeated 106x across cluster][0m
[36m(train_cnn_ray_tune pid=3216747)[0m 
[1m181/655[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m25s[0m 53ms/step - accuracy: 0.4870 - loss: 1.1677[32m [repeated 105x across cluster][0m
[36m(train_cnn_ray_tune pid=3216750)[0m 
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m19s[0m 57ms/step - accuracy: 0.4346 - loss: 1.2700 - val_accuracy: 0.4505 - val_loss: 1.2503
[36m(train_cnn_ray_tune pid=3216743)[0m 
[1m655/655[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m29s[0m 44ms/step - accuracy: 0.4008 - loss: 1.3610 - val_accuracy: 0.4747 - val_loss: 1.2147
[36m(train_cnn_ray_tune pid=3216752)[0m 
[1m480/655[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m10s[0m 58ms/step - accuracy: 0.4144 - loss: 1.4351
[1m481/655[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m10s[0m 58ms/step - accuracy: 0.4144 - loss: 1.4350
[1m482/655[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m10s[0m 58ms/step - accuracy: 0.4144 - loss: 1.4350
[36m(train_cnn_ray_tune pid=3216743)[0m 
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Trial status: 8 TERMINATED | 12 RUNNING
Current time: 2025-11-07 16:43:58. Total running time: 7min 0s
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     num_resblocks     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_987ea    RUNNING              3   adam            tanh                                   16                 64                  3                 1          0.000124674         27                                              │
│ trial_987ea    RUNNING              2   rmsprop         tanh                                   16                 64                  3                 1          8.8649e-06          23                                              │
│ trial_987ea    RUNNING              3   rmsprop         relu                                   32                 32                  3                 0          6.42207e-06         25                                              │
│ trial_987ea    RUNNING              3   rmsprop         relu                                   16                 64                  5                 1          6.20968e-06         17                                              │
│ trial_987ea    RUNNING              3   rmsprop         tanh                                   32                 32                  3                 0          2.14512e-05         19                                              │
│ trial_987ea    RUNNING              3   adam            relu                                   16                 64                  5                 0          3.57777e-05         28                                              │
│ trial_987ea    RUNNING              3   rmsprop         tanh                                   16                 64                  3                 1          4.71571e-05         22                                              │
│ trial_987ea    RUNNING              3   adam            relu                                   16                 64                  5                 0          9.94182e-05         27                                              │
│ trial_987ea    RUNNING              2   adam            tanh                                   16                 16                  3                 0          0.000100097         16                                              │
│ trial_987ea    RUNNING              2   adam            relu                                   32                 64                  3                 1          1.89531e-05         28                                              │
│ trial_987ea    RUNNING              3   rmsprop         relu                                   16                 64                  5                 1          0.000102887         19                                              │
│ trial_987ea    RUNNING              2   rmsprop         relu                                   16                 16                  3                 0          4.74263e-05         18                                              │
│ trial_987ea    TERMINATED           2   adam            tanh                                   16                 32                  3                 0          1.76369e-05         22        1            395.874         0.473315 │
│ trial_987ea    TERMINATED           3   rmsprop         tanh                                   32                 16                  3                 1          0.0001207           28        1            393.491         0.497542 │
│ trial_987ea    TERMINATED           2   adam            tanh                                   32                 16                  3                 1          0.000103558         26        1            404.185         0.506671 │
│ trial_987ea    TERMINATED           2   rmsprop         tanh                                   32                 16                  5                 0          0.000131282         20        1            325.689         0.514396 │
│ trial_987ea    TERMINATED           2   rmsprop         relu                                   32                 32                  5                 0          4.30906e-05         29        1            288.299         0.505618 │
│ trial_987ea    TERMINATED           2   rmsprop         tanh                                   32                 16                  3                 0          2.39155e-05         26        1            383.124         0.484902 │
│ trial_987ea    TERMINATED           2   adam            relu                                   32                 16                  3                 0          4.86659e-05         23        1            390.349         0.517205 │
│ trial_987ea    TERMINATED           3   adam            tanh                                   32                 64                  5                 1          0.000197693         21        1            361.388         0.484199 │
╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
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[36m(train_cnn_ray_tune pid=3216743)[0m 
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[36m(train_cnn_ray_tune pid=3216743)[0m 
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[36m(train_cnn_ray_tune pid=3216746)[0m 
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[36m(train_cnn_ray_tune pid=3216752)[0m 
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[36m(train_cnn_ray_tune pid=3216757)[0m 
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[36m(train_cnn_ray_tune pid=3216753)[0m 
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[36m(train_cnn_ray_tune pid=3216747)[0m 
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[36m(train_cnn_ray_tune pid=3216746)[0m 
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[36m(train_cnn_ray_tune pid=3216750)[0m 
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[36m(train_cnn_ray_tune pid=3216750)[0m Epoch 19/19[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3216745)[0m 
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[36m(train_cnn_ray_tune pid=3216753)[0m 
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[36m(train_cnn_ray_tune pid=3216750)[0m 
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[36m(train_cnn_ray_tune pid=3216752)[0m 
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[36m(train_cnn_ray_tune pid=3216750)[0m 
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[36m(train_cnn_ray_tune pid=3216753)[0m 
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[36m(train_cnn_ray_tune pid=3216741)[0m 
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[36m(train_cnn_ray_tune pid=3216741)[0m Epoch 19/25
[36m(train_cnn_ray_tune pid=3216753)[0m 
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[36m(train_cnn_ray_tune pid=3216743)[0m 
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[36m(train_cnn_ray_tune pid=3216740)[0m 
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[36m(train_cnn_ray_tune pid=3216757)[0m 
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[36m(train_cnn_ray_tune pid=3216753)[0m 
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[36m(train_cnn_ray_tune pid=3216741)[0m 
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[36m(train_cnn_ray_tune pid=3216741)[0m 
[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m40s[0m 124ms/step - accuracy: 0.5312 - loss: 1.2922
[36m(train_cnn_ray_tune pid=3216745)[0m 
[1m655/655[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m16s[0m 24ms/step - accuracy: 0.4554 - loss: 1.2069 - val_accuracy: 0.5249 - val_loss: 1.1178
[36m(train_cnn_ray_tune pid=3216745)[0m Epoch 18/18
[36m(train_cnn_ray_tune pid=3216745)[0m 
[1m  1/655[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:09[0m 106ms/step - accuracy: 0.5000 - loss: 1.1447
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[36m(train_cnn_ray_tune pid=3216743)[0m 
[1m  1/655[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m59s[0m 90ms/step - accuracy: 0.5625 - loss: 1.1843
[36m(train_cnn_ray_tune pid=3216747)[0m 
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[36m(train_cnn_ray_tune pid=3216753)[0m 
[1m503/655[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.4651 - loss: 1.1829
[1m505/655[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.4651 - loss: 1.1829
[1m507/655[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.4651 - loss: 1.1829
[36m(train_cnn_ray_tune pid=3216750)[0m 
[1m283/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m1s[0m 40ms/step - accuracy: 0.4419 - loss: 1.2494
[1m284/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m1s[0m 40ms/step - accuracy: 0.4419 - loss: 1.2494[32m [repeated 110x across cluster][0m
[36m(train_cnn_ray_tune pid=3216756)[0m 
[1m479/655[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m7s[0m 41ms/step - accuracy: 0.5292 - loss: 1.0699[32m [repeated 99x across cluster][0m
[36m(train_cnn_ray_tune pid=3216743)[0m 
[1m 92/655[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m18s[0m 32ms/step - accuracy: 0.3890 - loss: 1.3854
[1m 94/655[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m18s[0m 32ms/step - accuracy: 0.3889 - loss: 1.3850[32m [repeated 94x across cluster][0m
[36m(train_cnn_ray_tune pid=3216752)[0m 
[1m347/655[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m13s[0m 44ms/step - accuracy: 0.4244 - loss: 1.4124[32m [repeated 144x across cluster][0m
[36m(train_cnn_ray_tune pid=3216746)[0m 
[1m  3/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 41ms/step - accuracy: 0.4115 - loss: 1.3595 
[1m  5/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 36ms/step - accuracy: 0.4372 - loss: 1.3076
[36m(train_cnn_ray_tune pid=3216746)[0m 
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m13s[0m 39ms/step - accuracy: 0.5145 - loss: 1.1233 - val_accuracy: 0.5151 - val_loss: 1.1509[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3216746)[0m Epoch 24/28[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3216745)[0m 
[1m228/655[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m9s[0m 23ms/step - accuracy: 0.4781 - loss: 1.1940 
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[36m(train_cnn_ray_tune pid=3216746)[0m 
[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m37s[0m 116ms/step - accuracy: 0.3438 - loss: 1.5337[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3216757)[0m 
[1m451/655[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m9s[0m 45ms/step - accuracy: 0.5047 - loss: 1.1065
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[36m(train_cnn_ray_tune pid=3216750)[0m 
[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m30s[0m 639ms/step
[36m(train_cnn_ray_tune pid=3216750)[0m 
[1m 5/49[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 17ms/step  
[36m(train_cnn_ray_tune pid=3216750)[0m 
[1m 9/49[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 17ms/step
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[36m(train_cnn_ray_tune pid=3216750)[0m 
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[36m(train_cnn_ray_tune pid=3216750)[0m 
[1m18/49[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 19ms/step
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[36m(train_cnn_ray_tune pid=3216750)[0m 
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[36m(train_cnn_ray_tune pid=3216750)[0m 
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[36m(train_cnn_ray_tune pid=3216757)[0m 
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[36m(train_cnn_ray_tune pid=3216752)[0m 
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[36m(train_cnn_ray_tune pid=3216750)[0m 
[1m32/49[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 19ms/step
[1m35/49[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 19ms/step
[36m(train_cnn_ray_tune pid=3216750)[0m 
[1m38/49[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 19ms/step
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[36m(train_cnn_ray_tune pid=3216750)[0m 
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[36m(train_cnn_ray_tune pid=3216753)[0m 
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[36m(train_cnn_ray_tune pid=3216743)[0m 
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[36m(train_cnn_ray_tune pid=3216747)[0m 
[1m173/655[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m20s[0m 43ms/step - accuracy: 0.5025 - loss: 1.1290[32m [repeated 133x across cluster][0m
[36m(train_cnn_ray_tune pid=3216750)[0m 
[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 57ms/step
[36m(train_cnn_ray_tune pid=3216753)[0m 
[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 51ms/step
Trial status: 8 TERMINATED | 12 RUNNING
Current time: 2025-11-07 16:44:28. Total running time: 7min 30s
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     num_resblocks     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_987ea    RUNNING              3   adam            tanh                                   16                 64                  3                 1          0.000124674         27                                              │
│ trial_987ea    RUNNING              2   rmsprop         tanh                                   16                 64                  3                 1          8.8649e-06          23                                              │
│ trial_987ea    RUNNING              3   rmsprop         relu                                   32                 32                  3                 0          6.42207e-06         25                                              │
│ trial_987ea    RUNNING              3   rmsprop         relu                                   16                 64                  5                 1          6.20968e-06         17                                              │
│ trial_987ea    RUNNING              3   rmsprop         tanh                                   32                 32                  3                 0          2.14512e-05         19                                              │
│ trial_987ea    RUNNING              3   adam            relu                                   16                 64                  5                 0          3.57777e-05         28                                              │
│ trial_987ea    RUNNING              3   rmsprop         tanh                                   16                 64                  3                 1          4.71571e-05         22                                              │
│ trial_987ea    RUNNING              3   adam            relu                                   16                 64                  5                 0          9.94182e-05         27                                              │
│ trial_987ea    RUNNING              2   adam            tanh                                   16                 16                  3                 0          0.000100097         16                                              │
│ trial_987ea    RUNNING              2   adam            relu                                   32                 64                  3                 1          1.89531e-05         28                                              │
│ trial_987ea    RUNNING              3   rmsprop         relu                                   16                 64                  5                 1          0.000102887         19                                              │
│ trial_987ea    RUNNING              2   rmsprop         relu                                   16                 16                  3                 0          4.74263e-05         18                                              │
│ trial_987ea    TERMINATED           2   adam            tanh                                   16                 32                  3                 0          1.76369e-05         22        1            395.874         0.473315 │
│ trial_987ea    TERMINATED           3   rmsprop         tanh                                   32                 16                  3                 1          0.0001207           28        1            393.491         0.497542 │
│ trial_987ea    TERMINATED           2   adam            tanh                                   32                 16                  3                 1          0.000103558         26        1            404.185         0.506671 │
│ trial_987ea    TERMINATED           2   rmsprop         tanh                                   32                 16                  5                 0          0.000131282         20        1            325.689         0.514396 │
│ trial_987ea    TERMINATED           2   rmsprop         relu                                   32                 32                  5                 0          4.30906e-05         29        1            288.299         0.505618 │
│ trial_987ea    TERMINATED           2   rmsprop         tanh                                   32                 16                  3                 0          2.39155e-05         26        1            383.124         0.484902 │
│ trial_987ea    TERMINATED           2   adam            relu                                   32                 16                  3                 0          4.86659e-05         23        1            390.349         0.517205 │
│ trial_987ea    TERMINATED           3   adam            tanh                                   32                 64                  5                 1          0.000197693         21        1            361.388         0.484199 │
╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3216741)[0m 
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m16s[0m 47ms/step - accuracy: 0.4194 - loss: 1.3883 - val_accuracy: 0.4695 - val_loss: 1.2222[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3216741)[0m Epoch 20/25
[36m(train_cnn_ray_tune pid=3216741)[0m 
[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m33s[0m 104ms/step - accuracy: 0.4375 - loss: 1.4200
[1m  3/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 40ms/step - accuracy: 0.4358 - loss: 1.3504 
[36m(train_cnn_ray_tune pid=3216750)[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=3216750)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=3216752)[0m 
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Trial trial_987ea finished iteration 1 at 2025-11-07 16:44:29. Total running time: 7min 31s
╭─────────────────────────────────────╮
│ Trial trial_987ea result            │
├─────────────────────────────────────┤
│ checkpoint_dir_name                 │
│ time_this_iter_s             448.52 │
│ time_total_s                 448.52 │
│ training_iteration                1 │
│ val_accuracy                 0.4842 │
╰─────────────────────────────────────╯

Trial trial_987ea completed after 1 iterations at 2025-11-07 16:44:29. Total running time: 7min 31s

Trial trial_987ea finished iteration 1 at 2025-11-07 16:44:29. Total running time: 7min 31s
╭──────────────────────────────────────╮
│ Trial trial_987ea result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             448.547 │
│ time_total_s                 448.547 │
│ training_iteration                 1 │
│ val_accuracy                 0.45927 │
╰──────────────────────────────────────╯

Trial trial_987ea completed after 1 iterations at 2025-11-07 16:44:29. Total running time: 7min 31s
[36m(train_cnn_ray_tune pid=3216753)[0m 
[1m 6/49[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 12ms/step  
[36m(train_cnn_ray_tune pid=3216750)[0m 
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 19ms/step[32m [repeated 35x across cluster][0m
[36m(train_cnn_ray_tune pid=3216745)[0m 
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[36m(train_cnn_ray_tune pid=3216752)[0m 
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[36m(train_cnn_ray_tune pid=3216747)[0m 
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[1m310/655[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m14s[0m 41ms/step - accuracy: 0.5021 - loss: 1.1318[32m [repeated 40x across cluster][0m
[36m(train_cnn_ray_tune pid=3216747)[0m 
[1m306/655[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m14s[0m 41ms/step - accuracy: 0.5021 - loss: 1.1318[32m [repeated 51x across cluster][0m
[36m(train_cnn_ray_tune pid=3216756)[0m Epoch 10/19
[36m(train_cnn_ray_tune pid=3216756)[0m 
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[36m(train_cnn_ray_tune pid=3216756)[0m 
[1m  3/655[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m27s[0m 43ms/step - accuracy: 0.6042 - loss: 1.0434  
[36m(train_cnn_ray_tune pid=3216756)[0m 
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[36m(train_cnn_ray_tune pid=3216746)[0m 
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[36m(train_cnn_ray_tune pid=3216746)[0m 
[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m29s[0m 91ms/step - accuracy: 0.5625 - loss: 1.0898
[1m  4/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 23ms/step - accuracy: 0.5905 - loss: 1.0225 
[36m(train_cnn_ray_tune pid=3216745)[0m 
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[36m(train_cnn_ray_tune pid=3216745)[0m 
[1m 7/49[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 8ms/step   
[36m(train_cnn_ray_tune pid=3216745)[0m 
[1m13/49[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 8ms/step
[1m20/49[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 8ms/step
[36m(train_cnn_ray_tune pid=3216745)[0m 
[1m29/49[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 8ms/step
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[36m(train_cnn_ray_tune pid=3216745)[0m 
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[36m(train_cnn_ray_tune pid=3216745)[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=3216745)[0m   _log_deprecation_warning([32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3216747)[0m 
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Trial trial_987ea finished iteration 1 at 2025-11-07 16:44:37. Total running time: 7min 39s
╭──────────────────────────────────────╮
│ Trial trial_987ea result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             456.392 │
│ time_total_s                 456.392 │
│ training_iteration                 1 │
│ val_accuracy                 0.52353 │
╰──────────────────────────────────────╯

Trial trial_987ea completed after 1 iterations at 2025-11-07 16:44:37. Total running time: 7min 39s
[36m(train_cnn_ray_tune pid=3216745)[0m 
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[36m(train_cnn_ray_tune pid=3216755)[0m 
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[36m(train_cnn_ray_tune pid=3216756)[0m 
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[36m(train_cnn_ray_tune pid=3216756)[0m 
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[36m(train_cnn_ray_tune pid=3216752)[0m Epoch 10/17[32m [repeated 3x across cluster][0m
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[36m(train_cnn_ray_tune pid=3216752)[0m 
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[36m(train_cnn_ray_tune pid=3216740)[0m 
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[36m(train_cnn_ray_tune pid=3216740)[0m 
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[36m(train_cnn_ray_tune pid=3216740)[0m 
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[36m(train_cnn_ray_tune pid=3216746)[0m 
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[36m(train_cnn_ray_tune pid=3216746)[0m 
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[36m(train_cnn_ray_tune pid=3216752)[0m 
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[36m(train_cnn_ray_tune pid=3216743)[0m 
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[36m(train_cnn_ray_tune pid=3216741)[0m Epoch 21/25[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3216743)[0m 
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[36m(train_cnn_ray_tune pid=3216743)[0m 
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[36m(train_cnn_ray_tune pid=3216741)[0m 
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[36m(train_cnn_ray_tune pid=3216755)[0m 
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[36m(train_cnn_ray_tune pid=3216741)[0m 
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[36m(train_cnn_ray_tune pid=3216741)[0m 
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[36m(train_cnn_ray_tune pid=3216746)[0m 
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[1m  5/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 32ms/step - accuracy: 0.5127 - loss: 1.0313
[36m(train_cnn_ray_tune pid=3216756)[0m 
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[36m(train_cnn_ray_tune pid=3216752)[0m 
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[36m(train_cnn_ray_tune pid=3216746)[0m Epoch 26/28
[36m(train_cnn_ray_tune pid=3216746)[0m 
[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m40s[0m 124ms/step - accuracy: 0.4375 - loss: 1.0652
[36m(train_cnn_ray_tune pid=3216747)[0m Epoch 11/22
[36m(train_cnn_ray_tune pid=3216747)[0m 
[1m  1/655[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:16[0m 118ms/step - accuracy: 0.6875 - loss: 0.9448
[36m(train_cnn_ray_tune pid=3216747)[0m 
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[36m(train_cnn_ray_tune pid=3216747)[0m 
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[36m(train_cnn_ray_tune pid=3216743)[0m 
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[36m(train_cnn_ray_tune pid=3216746)[0m 
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[36m(train_cnn_ray_tune pid=3216756)[0m 
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[36m(train_cnn_ray_tune pid=3216755)[0m 
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[36m(train_cnn_ray_tune pid=3216756)[0m 
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[36m(train_cnn_ray_tune pid=3216752)[0m 
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[36m(train_cnn_ray_tune pid=3216752)[0m 
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[36m(train_cnn_ray_tune pid=3216746)[0m 
[1m259/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m2s[0m 36ms/step - accuracy: 0.5110 - loss: 1.1184
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[36m(train_cnn_ray_tune pid=3216756)[0m 
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[36m(train_cnn_ray_tune pid=3216755)[0m 
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[36m(train_cnn_ray_tune pid=3216757)[0m 
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[36m(train_cnn_ray_tune pid=3216752)[0m 
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[36m(train_cnn_ray_tune pid=3216741)[0m 
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[36m(train_cnn_ray_tune pid=3216741)[0m Epoch 22/25
[36m(train_cnn_ray_tune pid=3216741)[0m 
[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m44s[0m 137ms/step - accuracy: 0.1562 - loss: 1.9769
[36m(train_cnn_ray_tune pid=3216741)[0m 
[1m  3/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m14s[0m 43ms/step - accuracy: 0.2309 - loss: 1.7538 

Trial status: 11 TERMINATED | 9 RUNNING
Current time: 2025-11-07 16:44:58. Total running time: 8min 1s
Logical resource usage: 9.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     num_resblocks     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_987ea    RUNNING              3   adam            tanh                                   16                 64                  3                 1          0.000124674         27                                              │
│ trial_987ea    RUNNING              2   rmsprop         tanh                                   16                 64                  3                 1          8.8649e-06          23                                              │
│ trial_987ea    RUNNING              3   rmsprop         relu                                   32                 32                  3                 0          6.42207e-06         25                                              │
│ trial_987ea    RUNNING              3   rmsprop         relu                                   16                 64                  5                 1          6.20968e-06         17                                              │
│ trial_987ea    RUNNING              3   adam            relu                                   16                 64                  5                 0          3.57777e-05         28                                              │
│ trial_987ea    RUNNING              3   rmsprop         tanh                                   16                 64                  3                 1          4.71571e-05         22                                              │
│ trial_987ea    RUNNING              3   adam            relu                                   16                 64                  5                 0          9.94182e-05         27                                              │
│ trial_987ea    RUNNING              2   adam            relu                                   32                 64                  3                 1          1.89531e-05         28                                              │
│ trial_987ea    RUNNING              3   rmsprop         relu                                   16                 64                  5                 1          0.000102887         19                                              │
│ trial_987ea    TERMINATED           2   adam            tanh                                   16                 32                  3                 0          1.76369e-05         22        1            395.874         0.473315 │
│ trial_987ea    TERMINATED           3   rmsprop         tanh                                   32                 16                  3                 1          0.0001207           28        1            393.491         0.497542 │
│ trial_987ea    TERMINATED           2   adam            tanh                                   32                 16                  3                 1          0.000103558         26        1            404.185         0.506671 │
│ trial_987ea    TERMINATED           2   rmsprop         tanh                                   32                 16                  5                 0          0.000131282         20        1            325.689         0.514396 │
│ trial_987ea    TERMINATED           2   rmsprop         relu                                   32                 32                  5                 0          4.30906e-05         29        1            288.299         0.505618 │
│ trial_987ea    TERMINATED           2   rmsprop         tanh                                   32                 16                  3                 0          2.39155e-05         26        1            383.124         0.484902 │
│ trial_987ea    TERMINATED           3   rmsprop         tanh                                   32                 32                  3                 0          2.14512e-05         19        1            448.547         0.45927  │
│ trial_987ea    TERMINATED           2   adam            relu                                   32                 16                  3                 0          4.86659e-05         23        1            390.349         0.517205 │
│ trial_987ea    TERMINATED           2   adam            tanh                                   16                 16                  3                 0          0.000100097         16        1            448.52          0.484199 │
│ trial_987ea    TERMINATED           3   adam            tanh                                   32                 64                  5                 1          0.000197693         21        1            361.388         0.484199 │
│ trial_987ea    TERMINATED           2   rmsprop         relu                                   16                 16                  3                 0          4.74263e-05         18        1            456.392         0.523525 │
╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3216757)[0m 
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[36m(train_cnn_ray_tune pid=3216756)[0m 
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[36m(train_cnn_ray_tune pid=3216746)[0m 
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[36m(train_cnn_ray_tune pid=3216752)[0m 
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[36m(train_cnn_ray_tune pid=3216755)[0m 
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[36m(train_cnn_ray_tune pid=3216755)[0m 
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[36m(train_cnn_ray_tune pid=3216747)[0m 
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[36m(train_cnn_ray_tune pid=3216741)[0m 
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[36m(train_cnn_ray_tune pid=3216746)[0m 
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m14s[0m 42ms/step - accuracy: 0.5114 - loss: 1.1173 - val_accuracy: 0.5239 - val_loss: 1.1361
[36m(train_cnn_ray_tune pid=3216746)[0m Epoch 27/28
[36m(train_cnn_ray_tune pid=3216743)[0m 
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[36m(train_cnn_ray_tune pid=3216743)[0m Epoch 16/23
[36m(train_cnn_ray_tune pid=3216743)[0m 
[1m  1/655[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:01[0m 94ms/step - accuracy: 0.4375 - loss: 1.3617
[36m(train_cnn_ray_tune pid=3216743)[0m 
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[36m(train_cnn_ray_tune pid=3216757)[0m 
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[36m(train_cnn_ray_tune pid=3216741)[0m 
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[36m(train_cnn_ray_tune pid=3216756)[0m 
[1m  2/655[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m37s[0m 58ms/step - accuracy: 0.5938 - loss: 0.9444  
[1m  3/655[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m35s[0m 55ms/step - accuracy: 0.5694 - loss: 0.9713
[36m(train_cnn_ray_tune pid=3216757)[0m 
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[36m(train_cnn_ray_tune pid=3216752)[0m 
[1m609/655[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m2s[0m 47ms/step - accuracy: 0.4314 - loss: 1.3618[32m [repeated 164x across cluster][0m
[36m(train_cnn_ray_tune pid=3216747)[0m 
[1m388/655[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m11s[0m 45ms/step - accuracy: 0.5092 - loss: 1.1143
[1m389/655[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m11s[0m 45ms/step - accuracy: 0.5092 - loss: 1.1143
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[36m(train_cnn_ray_tune pid=3216756)[0m 
[1m 27/655[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m31s[0m 50ms/step - accuracy: 0.5331 - loss: 1.0384
[1m 28/655[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m31s[0m 51ms/step - accuracy: 0.5323 - loss: 1.0397[32m [repeated 34x across cluster][0m
[36m(train_cnn_ray_tune pid=3216747)[0m 
[1m408/655[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m11s[0m 45ms/step - accuracy: 0.5089 - loss: 1.1149[32m [repeated 56x across cluster][0m
[36m(train_cnn_ray_tune pid=3216756)[0m 
[1m655/655[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m33s[0m 51ms/step - accuracy: 0.5348 - loss: 1.0631 - val_accuracy: 0.5190 - val_loss: 1.1080
[36m(train_cnn_ray_tune pid=3216756)[0m Epoch 11/19
[36m(train_cnn_ray_tune pid=3216756)[0m 
[1m  1/655[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:19[0m 121ms/step - accuracy: 0.6250 - loss: 0.8900
[36m(train_cnn_ray_tune pid=3216757)[0m 
[1m608/655[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m2s[0m 50ms/step - accuracy: 0.5165 - loss: 1.0771
[1m609/655[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m2s[0m 50ms/step - accuracy: 0.5165 - loss: 1.0771
[1m610/655[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m2s[0m 50ms/step - accuracy: 0.5165 - loss: 1.0771[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3216752)[0m 
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[36m(train_cnn_ray_tune pid=3216752)[0m Epoch 11/17
[36m(train_cnn_ray_tune pid=3216743)[0m 
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[36m(train_cnn_ray_tune pid=3216747)[0m 
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[36m(train_cnn_ray_tune pid=3216747)[0m 
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[36m(train_cnn_ray_tune pid=3216756)[0m 
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[36m(train_cnn_ray_tune pid=3216741)[0m 
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[36m(train_cnn_ray_tune pid=3216752)[0m 
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[36m(train_cnn_ray_tune pid=3216756)[0m 
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[36m(train_cnn_ray_tune pid=3216755)[0m 
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[36m(train_cnn_ray_tune pid=3216746)[0m 
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[36m(train_cnn_ray_tune pid=3216755)[0m 
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[36m(train_cnn_ray_tune pid=3216755)[0m Epoch 9/28[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3216746)[0m 
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[36m(train_cnn_ray_tune pid=3216743)[0m 
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[36m(train_cnn_ray_tune pid=3216746)[0m 
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[36m(train_cnn_ray_tune pid=3216755)[0m 
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[36m(train_cnn_ray_tune pid=3216752)[0m 
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[36m(train_cnn_ray_tune pid=3216740)[0m 
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[36m(train_cnn_ray_tune pid=3216741)[0m 
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[36m(train_cnn_ray_tune pid=3216757)[0m 
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[36m(train_cnn_ray_tune pid=3216747)[0m 
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[36m(train_cnn_ray_tune pid=3216747)[0m 
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[36m(train_cnn_ray_tune pid=3216747)[0m Epoch 12/22
[36m(train_cnn_ray_tune pid=3216746)[0m 
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[36m(train_cnn_ray_tune pid=3216741)[0m 
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[36m(train_cnn_ray_tune pid=3216756)[0m 
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[36m(train_cnn_ray_tune pid=3216752)[0m 
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[36m(train_cnn_ray_tune pid=3216756)[0m 
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[36m(train_cnn_ray_tune pid=3216740)[0m 
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[36m(train_cnn_ray_tune pid=3216743)[0m 
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[36m(train_cnn_ray_tune pid=3216743)[0m 
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[36m(train_cnn_ray_tune pid=3216752)[0m 
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Trial status: 11 TERMINATED | 9 RUNNING
Current time: 2025-11-07 16:45:29. Total running time: 8min 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     num_resblocks     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_987ea    RUNNING              3   adam            tanh                                   16                 64                  3                 1          0.000124674         27                                              │
│ trial_987ea    RUNNING              2   rmsprop         tanh                                   16                 64                  3                 1          8.8649e-06          23                                              │
│ trial_987ea    RUNNING              3   rmsprop         relu                                   32                 32                  3                 0          6.42207e-06         25                                              │
│ trial_987ea    RUNNING              3   rmsprop         relu                                   16                 64                  5                 1          6.20968e-06         17                                              │
│ trial_987ea    RUNNING              3   adam            relu                                   16                 64                  5                 0          3.57777e-05         28                                              │
│ trial_987ea    RUNNING              3   rmsprop         tanh                                   16                 64                  3                 1          4.71571e-05         22                                              │
│ trial_987ea    RUNNING              3   adam            relu                                   16                 64                  5                 0          9.94182e-05         27                                              │
│ trial_987ea    RUNNING              2   adam            relu                                   32                 64                  3                 1          1.89531e-05         28                                              │
│ trial_987ea    RUNNING              3   rmsprop         relu                                   16                 64                  5                 1          0.000102887         19                                              │
│ trial_987ea    TERMINATED           2   adam            tanh                                   16                 32                  3                 0          1.76369e-05         22        1            395.874         0.473315 │
│ trial_987ea    TERMINATED           3   rmsprop         tanh                                   32                 16                  3                 1          0.0001207           28        1            393.491         0.497542 │
│ trial_987ea    TERMINATED           2   adam            tanh                                   32                 16                  3                 1          0.000103558         26        1            404.185         0.506671 │
│ trial_987ea    TERMINATED           2   rmsprop         tanh                                   32                 16                  5                 0          0.000131282         20        1            325.689         0.514396 │
│ trial_987ea    TERMINATED           2   rmsprop         relu                                   32                 32                  5                 0          4.30906e-05         29        1            288.299         0.505618 │
│ trial_987ea    TERMINATED           2   rmsprop         tanh                                   32                 16                  3                 0          2.39155e-05         26        1            383.124         0.484902 │
│ trial_987ea    TERMINATED           3   rmsprop         tanh                                   32                 32                  3                 0          2.14512e-05         19        1            448.547         0.45927  │
│ trial_987ea    TERMINATED           2   adam            relu                                   32                 16                  3                 0          4.86659e-05         23        1            390.349         0.517205 │
│ trial_987ea    TERMINATED           2   adam            tanh                                   16                 16                  3                 0          0.000100097         16        1            448.52          0.484199 │
│ trial_987ea    TERMINATED           3   adam            tanh                                   32                 64                  5                 1          0.000197693         21        1            361.388         0.484199 │
│ trial_987ea    TERMINATED           2   rmsprop         relu                                   16                 16                  3                 0          4.74263e-05         18        1            456.392         0.523525 │
╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3216741)[0m 
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[36m(train_cnn_ray_tune pid=3216746)[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=3216746)[0m   _log_deprecation_warning(
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[36m(train_cnn_ray_tune pid=3216746)[0m 
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[36m(train_cnn_ray_tune pid=3216755)[0m 
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[36m(train_cnn_ray_tune pid=3216746)[0m 
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[36m(train_cnn_ray_tune pid=3216746)[0m 
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[36m(train_cnn_ray_tune pid=3216746)[0m 
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[36m(train_cnn_ray_tune pid=3216746)[0m 
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[36m(train_cnn_ray_tune pid=3216746)[0m 
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[36m(train_cnn_ray_tune pid=3216746)[0m 
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[36m(train_cnn_ray_tune pid=3216746)[0m 
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[36m(train_cnn_ray_tune pid=3216755)[0m 
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[36m(train_cnn_ray_tune pid=3216746)[0m 
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[36m(train_cnn_ray_tune pid=3216746)[0m 
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[36m(train_cnn_ray_tune pid=3216746)[0m 
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[36m(train_cnn_ray_tune pid=3216746)[0m 
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Trial trial_987ea finished iteration 1 at 2025-11-07 16:45:31. Total running time: 8min 33s
╭──────────────────────────────────────╮
│ Trial trial_987ea result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             510.209 │
│ time_total_s                 510.209 │
│ training_iteration                 1 │
│ val_accuracy                 0.53722 │
╰──────────────────────────────────────╯

Trial trial_987ea completed after 1 iterations at 2025-11-07 16:45:31. Total running time: 8min 33s
[36m(train_cnn_ray_tune pid=3216741)[0m 
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[36m(train_cnn_ray_tune pid=3216741)[0m Epoch 24/25
[36m(train_cnn_ray_tune pid=3216741)[0m 
[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m40s[0m 123ms/step - accuracy: 0.5625 - loss: 1.2048
[36m(train_cnn_ray_tune pid=3216756)[0m 
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[36m(train_cnn_ray_tune pid=3216756)[0m 
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[36m(train_cnn_ray_tune pid=3216752)[0m 
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[36m(train_cnn_ray_tune pid=3216740)[0m 
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[36m(train_cnn_ray_tune pid=3216755)[0m 
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[36m(train_cnn_ray_tune pid=3216757)[0m 
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[36m(train_cnn_ray_tune pid=3216741)[0m 
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[36m(train_cnn_ray_tune pid=3216752)[0m 
[1m546/655[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m5s[0m 46ms/step - accuracy: 0.4217 - loss: 1.3809[32m [repeated 96x across cluster][0m
[36m(train_cnn_ray_tune pid=3216740)[0m 
[1m452/655[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m9s[0m 49ms/step - accuracy: 0.5467 - loss: 1.0487 
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[36m(train_cnn_ray_tune pid=3216743)[0m 
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[36m(train_cnn_ray_tune pid=3216747)[0m 
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[36m(train_cnn_ray_tune pid=3216747)[0m 
[1m383/655[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m12s[0m 44ms/step - accuracy: 0.5304 - loss: 1.0894[32m [repeated 79x across cluster][0m
[36m(train_cnn_ray_tune pid=3216756)[0m 
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[36m(train_cnn_ray_tune pid=3216756)[0m Epoch 12/19
[36m(train_cnn_ray_tune pid=3216755)[0m 
[1m511/655[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m7s[0m 52ms/step - accuracy: 0.4893 - loss: 1.1339
[1m512/655[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m7s[0m 52ms/step - accuracy: 0.4893 - loss: 1.1339
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[36m(train_cnn_ray_tune pid=3216756)[0m 
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[36m(train_cnn_ray_tune pid=3216756)[0m 
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[36m(train_cnn_ray_tune pid=3216747)[0m 
[1m479/655[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m7s[0m 44ms/step - accuracy: 0.5295 - loss: 1.0899
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[36m(train_cnn_ray_tune pid=3216755)[0m 
[1m554/655[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m5s[0m 52ms/step - accuracy: 0.4894 - loss: 1.1344[32m [repeated 181x across cluster][0m
[36m(train_cnn_ray_tune pid=3216756)[0m 
[1m 78/655[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m24s[0m 42ms/step - accuracy: 0.5459 - loss: 1.0498
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[36m(train_cnn_ray_tune pid=3216756)[0m 
[1m 76/655[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m24s[0m 42ms/step - accuracy: 0.5457 - loss: 1.0502[32m [repeated 26x across cluster][0m
[36m(train_cnn_ray_tune pid=3216741)[0m 
[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m30s[0m 94ms/step - accuracy: 0.5000 - loss: 1.2877
[1m  3/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 36ms/step - accuracy: 0.4358 - loss: 1.4236
[36m(train_cnn_ray_tune pid=3216741)[0m 
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m15s[0m 47ms/step - accuracy: 0.4234 - loss: 1.3608 - val_accuracy: 0.4842 - val_loss: 1.2093
[36m(train_cnn_ray_tune pid=3216741)[0m Epoch 25/25
[36m(train_cnn_ray_tune pid=3216752)[0m 
[1m655/655[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m33s[0m 51ms/step - accuracy: 0.4232 - loss: 1.3778 - val_accuracy: 0.4895 - val_loss: 1.2309
[36m(train_cnn_ray_tune pid=3216752)[0m Epoch 12/17
[36m(train_cnn_ray_tune pid=3216752)[0m 
[1m  1/655[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:02[0m 95ms/step - accuracy: 0.6250 - loss: 1.2537
[36m(train_cnn_ray_tune pid=3216752)[0m 
[1m  3/655[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m24s[0m 37ms/step - accuracy: 0.5069 - loss: 1.5310 
[1m  5/655[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m23s[0m 35ms/step - accuracy: 0.4935 - loss: 1.5014
[36m(train_cnn_ray_tune pid=3216741)[0m 
[1m106/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m6s[0m 28ms/step - accuracy: 0.4090 - loss: 1.3886
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[36m(train_cnn_ray_tune pid=3216747)[0m 
[1m638/655[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 41ms/step - accuracy: 0.5276 - loss: 1.0909[32m [repeated 94x across cluster][0m
[36m(train_cnn_ray_tune pid=3216757)[0m 
[1m  3/655[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m28s[0m 43ms/step - accuracy: 0.4931 - loss: 1.0197  
[36m(train_cnn_ray_tune pid=3216740)[0m 
[1m 43/655[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m20s[0m 33ms/step - accuracy: 0.5670 - loss: 1.0292
[1m 45/655[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m19s[0m 33ms/step - accuracy: 0.5655 - loss: 1.0303[32m [repeated 61x across cluster][0m
[36m(train_cnn_ray_tune pid=3216756)[0m 
[1m235/655[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m14s[0m 36ms/step - accuracy: 0.5542 - loss: 1.0380[32m [repeated 34x across cluster][0m
[36m(train_cnn_ray_tune pid=3216755)[0m 
[1m  1/655[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:25[0m 131ms/step - accuracy: 0.6250 - loss: 0.8349
[1m  2/655[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m32s[0m 50ms/step - accuracy: 0.6094 - loss: 0.9123  
[36m(train_cnn_ray_tune pid=3216755)[0m 
[1m655/655[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m36s[0m 55ms/step - accuracy: 0.4898 - loss: 1.1356 - val_accuracy: 0.5119 - val_loss: 1.1377[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=3216755)[0m Epoch 10/28[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=3216743)[0m 
[1m  1/655[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:04[0m 98ms/step - accuracy: 0.4375 - loss: 1.1764[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3216743)[0m 
[1m  3/655[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m16s[0m 26ms/step - accuracy: 0.3611 - loss: 1.3057 
[1m  5/655[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m17s[0m 27ms/step - accuracy: 0.3685 - loss: 1.3221[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3216740)[0m 
[1m111/655[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m20s[0m 38ms/step - accuracy: 0.5490 - loss: 1.0385
[1m112/655[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m20s[0m 38ms/step - accuracy: 0.5489 - loss: 1.0385
[1m113/655[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m20s[0m 38ms/step - accuracy: 0.5489 - loss: 1.0385
[36m(train_cnn_ray_tune pid=3216741)[0m 
[1m249/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m2s[0m 32ms/step - accuracy: 0.4131 - loss: 1.3822
[1m250/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m2s[0m 32ms/step - accuracy: 0.4131 - loss: 1.3821[32m [repeated 27x across cluster][0m
[36m(train_cnn_ray_tune pid=3216741)[0m 
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[36m(train_cnn_ray_tune pid=3216747)[0m 
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[36m(train_cnn_ray_tune pid=3216756)[0m 
[1m354/655[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m11s[0m 38ms/step - accuracy: 0.5574 - loss: 1.0320
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[36m(train_cnn_ray_tune pid=3216752)[0m 
[1m215/655[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m16s[0m 37ms/step - accuracy: 0.4594 - loss: 1.3270[32m [repeated 170x across cluster][0m
[36m(train_cnn_ray_tune pid=3216747)[0m 
[1m655/655[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m30s[0m 46ms/step - accuracy: 0.5274 - loss: 1.0911 - val_accuracy: 0.5095 - val_loss: 1.1934
[36m(train_cnn_ray_tune pid=3216747)[0m Epoch 13/22
[36m(train_cnn_ray_tune pid=3216747)[0m 
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[36m(train_cnn_ray_tune pid=3216755)[0m 
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[36m(train_cnn_ray_tune pid=3216756)[0m 
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[36m(train_cnn_ray_tune pid=3216752)[0m 
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Trial status: 12 TERMINATED | 8 RUNNING
Current time: 2025-11-07 16:45:59. Total running time: 9min 1s
Logical resource usage: 8.0/20 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     num_resblocks     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_987ea    RUNNING              3   adam            tanh                                   16                 64                  3                 1          0.000124674         27                                              │
│ trial_987ea    RUNNING              2   rmsprop         tanh                                   16                 64                  3                 1          8.8649e-06          23                                              │
│ trial_987ea    RUNNING              3   rmsprop         relu                                   32                 32                  3                 0          6.42207e-06         25                                              │
│ trial_987ea    RUNNING              3   rmsprop         relu                                   16                 64                  5                 1          6.20968e-06         17                                              │
│ trial_987ea    RUNNING              3   adam            relu                                   16                 64                  5                 0          3.57777e-05         28                                              │
│ trial_987ea    RUNNING              3   rmsprop         tanh                                   16                 64                  3                 1          4.71571e-05         22                                              │
│ trial_987ea    RUNNING              3   adam            relu                                   16                 64                  5                 0          9.94182e-05         27                                              │
│ trial_987ea    RUNNING              3   rmsprop         relu                                   16                 64                  5                 1          0.000102887         19                                              │
│ trial_987ea    TERMINATED           2   adam            tanh                                   16                 32                  3                 0          1.76369e-05         22        1            395.874         0.473315 │
│ trial_987ea    TERMINATED           3   rmsprop         tanh                                   32                 16                  3                 1          0.0001207           28        1            393.491         0.497542 │
│ trial_987ea    TERMINATED           2   adam            tanh                                   32                 16                  3                 1          0.000103558         26        1            404.185         0.506671 │
│ trial_987ea    TERMINATED           2   rmsprop         tanh                                   32                 16                  5                 0          0.000131282         20        1            325.689         0.514396 │
│ trial_987ea    TERMINATED           2   rmsprop         relu                                   32                 32                  5                 0          4.30906e-05         29        1            288.299         0.505618 │
│ trial_987ea    TERMINATED           2   rmsprop         tanh                                   32                 16                  3                 0          2.39155e-05         26        1            383.124         0.484902 │
│ trial_987ea    TERMINATED           3   rmsprop         tanh                                   32                 32                  3                 0          2.14512e-05         19        1            448.547         0.45927  │
│ trial_987ea    TERMINATED           2   adam            relu                                   32                 16                  3                 0          4.86659e-05         23        1            390.349         0.517205 │
│ trial_987ea    TERMINATED           2   adam            tanh                                   16                 16                  3                 0          0.000100097         16        1            448.52          0.484199 │
│ trial_987ea    TERMINATED           2   adam            relu                                   32                 64                  3                 1          1.89531e-05         28        1            510.209         0.537219 │
│ trial_987ea    TERMINATED           3   adam            tanh                                   32                 64                  5                 1          0.000197693         21        1            361.388         0.484199 │
│ trial_987ea    TERMINATED           2   rmsprop         relu                                   16                 16                  3                 0          4.74263e-05         18        1            456.392         0.523525 │
╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3216743)[0m 
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[36m(train_cnn_ray_tune pid=3216741)[0m 
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[36m(train_cnn_ray_tune pid=3216741)[0m 
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[36m(train_cnn_ray_tune pid=3216740)[0m 
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[36m(train_cnn_ray_tune pid=3216741)[0m 
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[36m(train_cnn_ray_tune pid=3216741)[0m 
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[36m(train_cnn_ray_tune pid=3216741)[0m 
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Trial trial_987ea finished iteration 1 at 2025-11-07 16:46:02. Total running time: 9min 4s
╭──────────────────────────────────────╮
│ Trial trial_987ea result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             541.383 │
│ time_total_s                 541.383 │
│ training_iteration                 1 │
│ val_accuracy                  0.4835 │
╰──────────────────────────────────────╯

[36m(train_cnn_ray_tune pid=3216741)[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=3216741)[0m   _log_deprecation_warning(
Trial trial_987ea completed after 1 iterations at 2025-11-07 16:46:02. Total running time: 9min 4s
[36m(train_cnn_ray_tune pid=3216756)[0m 
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[36m(train_cnn_ray_tune pid=3216755)[0m 
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[36m(train_cnn_ray_tune pid=3216755)[0m 
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[36m(train_cnn_ray_tune pid=3216743)[0m 
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[36m(train_cnn_ray_tune pid=3216740)[0m 
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[36m(train_cnn_ray_tune pid=3216755)[0m 
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[36m(train_cnn_ray_tune pid=3216747)[0m 
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[36m(train_cnn_ray_tune pid=3216747)[0m 
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[36m(train_cnn_ray_tune pid=3216756)[0m 
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[36m(train_cnn_ray_tune pid=3216756)[0m Epoch 13/19
[36m(train_cnn_ray_tune pid=3216756)[0m 
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[36m(train_cnn_ray_tune pid=3216756)[0m 
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[36m(train_cnn_ray_tune pid=3216755)[0m 
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[36m(train_cnn_ray_tune pid=3216757)[0m 
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[36m(train_cnn_ray_tune pid=3216740)[0m 
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[36m(train_cnn_ray_tune pid=3216743)[0m 
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[36m(train_cnn_ray_tune pid=3216756)[0m 
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[36m(train_cnn_ray_tune pid=3216743)[0m 
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[36m(train_cnn_ray_tune pid=3216743)[0m Epoch 19/23
[36m(train_cnn_ray_tune pid=3216752)[0m 
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[36m(train_cnn_ray_tune pid=3216752)[0m Epoch 13/17
[36m(train_cnn_ray_tune pid=3216752)[0m 
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[36m(train_cnn_ray_tune pid=3216752)[0m 
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[36m(train_cnn_ray_tune pid=3216755)[0m 
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[36m(train_cnn_ray_tune pid=3216747)[0m 
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[36m(train_cnn_ray_tune pid=3216743)[0m 
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[36m(train_cnn_ray_tune pid=3216756)[0m 
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[36m(train_cnn_ray_tune pid=3216752)[0m 
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[36m(train_cnn_ray_tune pid=3216740)[0m 
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[36m(train_cnn_ray_tune pid=3216747)[0m 
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[36m(train_cnn_ray_tune pid=3216757)[0m 
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[36m(train_cnn_ray_tune pid=3216757)[0m 
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[36m(train_cnn_ray_tune pid=3216757)[0m 
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[36m(train_cnn_ray_tune pid=3216757)[0m 
[1m13/49[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 15ms/step
[36m(train_cnn_ray_tune pid=3216757)[0m 
[1m17/49[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 15ms/step
[36m(train_cnn_ray_tune pid=3216757)[0m 
[1m21/49[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 15ms/step
[36m(train_cnn_ray_tune pid=3216757)[0m 
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[36m(train_cnn_ray_tune pid=3216757)[0m 
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[36m(train_cnn_ray_tune pid=3216757)[0m 
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[36m(train_cnn_ray_tune pid=3216757)[0m 
[1m41/49[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 15ms/step
[1m45/49[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 15ms/step
[36m(train_cnn_ray_tune pid=3216747)[0m 
[1m655/655[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m29s[0m 44ms/step - accuracy: 0.5296 - loss: 1.0832 - val_accuracy: 0.5070 - val_loss: 1.1448[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3216747)[0m Epoch 14/22[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3216747)[0m 
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[36m(train_cnn_ray_tune pid=3216757)[0m 
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 25ms/step
[36m(train_cnn_ray_tune pid=3216757)[0m 
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 26ms/step
[36m(train_cnn_ray_tune pid=3216755)[0m 
[1m  1/655[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:08[0m 104ms/step - accuracy: 0.5625 - loss: 1.1916
[36m(train_cnn_ray_tune pid=3216755)[0m 
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[36m(train_cnn_ray_tune pid=3216757)[0m 
[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 71ms/step
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[36m(train_cnn_ray_tune pid=3216757)[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=3216757)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=3216757)[0m 
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[36m(train_cnn_ray_tune pid=3216757)[0m 
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[36m(train_cnn_ray_tune pid=3216743)[0m 
[1m473/655[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m4s[0m 23ms/step - accuracy: 0.4133 - loss: 1.2968
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[36m(train_cnn_ray_tune pid=3216743)[0m 
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[36m(train_cnn_ray_tune pid=3216757)[0m 
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[36m(train_cnn_ray_tune pid=3216757)[0m 
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[36m(train_cnn_ray_tune pid=3216757)[0m 
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[36m(train_cnn_ray_tune pid=3216757)[0m 
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[36m(train_cnn_ray_tune pid=3216757)[0m 
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[36m(train_cnn_ray_tune pid=3216757)[0m 
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[36m(train_cnn_ray_tune pid=3216757)[0m 
[1m56/89[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 18ms/step
[1m59/89[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 18ms/step
[36m(train_cnn_ray_tune pid=3216757)[0m 
[1m62/89[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 18ms/step
[1m65/89[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 18ms/step
[36m(train_cnn_ray_tune pid=3216757)[0m 
[1m68/89[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 18ms/step
[36m(train_cnn_ray_tune pid=3216757)[0m 
[1m71/89[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 18ms/step
[36m(train_cnn_ray_tune pid=3216757)[0m 
[1m75/89[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 18ms/step
[36m(train_cnn_ray_tune pid=3216757)[0m 
[1m79/89[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 18ms/step
[36m(train_cnn_ray_tune pid=3216757)[0m 
[1m83/89[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 18ms/step
[36m(train_cnn_ray_tune pid=3216755)[0m 
[1m 40/655[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m25s[0m 41ms/step - accuracy: 0.5341 - loss: 1.0794
[1m 42/655[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m25s[0m 41ms/step - accuracy: 0.5341 - loss: 1.0789[32m [repeated 100x across cluster][0m
[36m(train_cnn_ray_tune pid=3216752)[0m 
[1m230/655[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m13s[0m 32ms/step - accuracy: 0.4300 - loss: 1.3612[32m [repeated 93x across cluster][0m
[36m(train_cnn_ray_tune pid=3216757)[0m 
[1m86/89[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 18ms/step

Trial trial_987ea finished iteration 1 at 2025-11-07 16:46:24. Total running time: 9min 26s
╭──────────────────────────────────────╮
│ Trial trial_987ea result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             563.387 │
│ time_total_s                 563.387 │
│ training_iteration                 1 │
│ val_accuracy                 0.53125 │
╰──────────────────────────────────────╯

Trial trial_987ea completed after 1 iterations at 2025-11-07 16:46:24. Total running time: 9min 26s
[36m(train_cnn_ray_tune pid=3216757)[0m 
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 19ms/step
[36m(train_cnn_ray_tune pid=3216755)[0m 
[1m655/655[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m33s[0m 50ms/step - accuracy: 0.5026 - loss: 1.1369 - val_accuracy: 0.5042 - val_loss: 1.1309
[36m(train_cnn_ray_tune pid=3216755)[0m Epoch 11/28
[36m(train_cnn_ray_tune pid=3216756)[0m 
[1m478/655[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m6s[0m 35ms/step - accuracy: 0.5671 - loss: 0.9943
[1m480/655[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m6s[0m 35ms/step - accuracy: 0.5671 - loss: 0.9943[32m [repeated 43x across cluster][0m
[36m(train_cnn_ray_tune pid=3216756)[0m 
[1m490/655[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m5s[0m 35ms/step - accuracy: 0.5671 - loss: 0.9943[32m [repeated 42x across cluster][0m
[36m(train_cnn_ray_tune pid=3216747)[0m 
[1m255/655[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m12s[0m 32ms/step - accuracy: 0.5168 - loss: 1.0851
[1m257/655[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m12s[0m 32ms/step - accuracy: 0.5168 - loss: 1.0851[32m [repeated 85x across cluster][0m
[36m(train_cnn_ray_tune pid=3216755)[0m 
[1m178/655[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m18s[0m 38ms/step - accuracy: 0.5205 - loss: 1.0917[32m [repeated 109x across cluster][0m

Trial status: 14 TERMINATED | 6 RUNNING
Current time: 2025-11-07 16:46:29. Total running time: 9min 31s
Logical resource usage: 6.0/20 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     num_resblocks     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_987ea    RUNNING              3   adam            tanh                                   16                 64                  3                 1          0.000124674         27                                              │
│ trial_987ea    RUNNING              2   rmsprop         tanh                                   16                 64                  3                 1          8.8649e-06          23                                              │
│ trial_987ea    RUNNING              3   rmsprop         relu                                   16                 64                  5                 1          6.20968e-06         17                                              │
│ trial_987ea    RUNNING              3   adam            relu                                   16                 64                  5                 0          3.57777e-05         28                                              │
│ trial_987ea    RUNNING              3   rmsprop         tanh                                   16                 64                  3                 1          4.71571e-05         22                                              │
│ trial_987ea    RUNNING              3   rmsprop         relu                                   16                 64                  5                 1          0.000102887         19                                              │
│ trial_987ea    TERMINATED           2   adam            tanh                                   16                 32                  3                 0          1.76369e-05         22        1            395.874         0.473315 │
│ trial_987ea    TERMINATED           3   rmsprop         tanh                                   32                 16                  3                 1          0.0001207           28        1            393.491         0.497542 │
│ trial_987ea    TERMINATED           2   adam            tanh                                   32                 16                  3                 1          0.000103558         26        1            404.185         0.506671 │
│ trial_987ea    TERMINATED           2   rmsprop         tanh                                   32                 16                  5                 0          0.000131282         20        1            325.689         0.514396 │
│ trial_987ea    TERMINATED           2   rmsprop         relu                                   32                 32                  5                 0          4.30906e-05         29        1            288.299         0.505618 │
│ trial_987ea    TERMINATED           3   rmsprop         relu                                   32                 32                  3                 0          6.42207e-06         25        1            541.383         0.483497 │
│ trial_987ea    TERMINATED           2   rmsprop         tanh                                   32                 16                  3                 0          2.39155e-05         26        1            383.124         0.484902 │
│ trial_987ea    TERMINATED           3   rmsprop         tanh                                   32                 32                  3                 0          2.14512e-05         19        1            448.547         0.45927  │
│ trial_987ea    TERMINATED           2   adam            relu                                   32                 16                  3                 0          4.86659e-05         23        1            390.349         0.517205 │
│ trial_987ea    TERMINATED           3   adam            relu                                   16                 64                  5                 0          9.94182e-05         27        1            563.387         0.53125  │
│ trial_987ea    TERMINATED           2   adam            tanh                                   16                 16                  3                 0          0.000100097         16        1            448.52          0.484199 │
│ trial_987ea    TERMINATED           2   adam            relu                                   32                 64                  3                 1          1.89531e-05         28        1            510.209         0.537219 │
│ trial_987ea    TERMINATED           3   adam            tanh                                   32                 64                  5                 1          0.000197693         21        1            361.388         0.484199 │
│ trial_987ea    TERMINATED           2   rmsprop         relu                                   16                 16                  3                 0          4.74263e-05         18        1            456.392         0.523525 │
╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3216743)[0m 
[1m655/655[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m18s[0m 27ms/step - accuracy: 0.4141 - loss: 1.2970 - val_accuracy: 0.4786 - val_loss: 1.1991
[36m(train_cnn_ray_tune pid=3216743)[0m Epoch 20/23
[36m(train_cnn_ray_tune pid=3216743)[0m 
[1m  1/655[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m58s[0m 90ms/step - accuracy: 0.3125 - loss: 1.3567
[1m  4/655[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 19ms/step - accuracy: 0.3815 - loss: 1.2759
[36m(train_cnn_ray_tune pid=3216752)[0m 
[1m482/655[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m5s[0m 33ms/step - accuracy: 0.4379 - loss: 1.3438
[1m484/655[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m5s[0m 33ms/step - accuracy: 0.4380 - loss: 1.3437[32m [repeated 61x across cluster][0m
[36m(train_cnn_ray_tune pid=3216747)[0m 
[1m373/655[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m9s[0m 32ms/step - accuracy: 0.5196 - loss: 1.0820[32m [repeated 66x across cluster][0m
[36m(train_cnn_ray_tune pid=3216755)[0m 
[1m314/655[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m12s[0m 37ms/step - accuracy: 0.5131 - loss: 1.1049
[1m316/655[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m12s[0m 37ms/step - accuracy: 0.5130 - loss: 1.1050[32m [repeated 68x across cluster][0m
[36m(train_cnn_ray_tune pid=3216755)[0m 
[1m320/655[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m12s[0m 37ms/step - accuracy: 0.5129 - loss: 1.1053[32m [repeated 74x across cluster][0m
[36m(train_cnn_ray_tune pid=3216756)[0m 
[1m655/655[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m25s[0m 39ms/step - accuracy: 0.5677 - loss: 0.9943 - val_accuracy: 0.4568 - val_loss: 1.1888
[36m(train_cnn_ray_tune pid=3216756)[0m Epoch 14/19
[36m(train_cnn_ray_tune pid=3216756)[0m 
[1m  1/655[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:09[0m 107ms/step - accuracy: 0.4375 - loss: 1.0734
[36m(train_cnn_ray_tune pid=3216756)[0m 
[1m  3/655[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m23s[0m 36ms/step - accuracy: 0.4375 - loss: 1.1429  
[1m  5/655[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m24s[0m 37ms/step - accuracy: 0.4319 - loss: 1.1912
[36m(train_cnn_ray_tune pid=3216752)[0m 
[1m647/655[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 33ms/step - accuracy: 0.4404 - loss: 1.3391
[1m649/655[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 33ms/step - accuracy: 0.4405 - loss: 1.3391[32m [repeated 140x across cluster][0m
[36m(train_cnn_ray_tune pid=3216755)[0m 
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[36m(train_cnn_ray_tune pid=3216756)[0m 
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[36m(train_cnn_ray_tune pid=3216756)[0m 
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[36m(train_cnn_ray_tune pid=3216752)[0m 
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[36m(train_cnn_ray_tune pid=3216752)[0m Epoch 14/17
[36m(train_cnn_ray_tune pid=3216752)[0m 
[1m  1/655[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m47s[0m 73ms/step - accuracy: 0.5000 - loss: 0.9895
[36m(train_cnn_ray_tune pid=3216740)[0m 
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[36m(train_cnn_ray_tune pid=3216740)[0m Epoch 14/27
[36m(train_cnn_ray_tune pid=3216740)[0m 
[1m  1/655[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m59s[0m 92ms/step - accuracy: 0.6250 - loss: 0.9078
[36m(train_cnn_ray_tune pid=3216755)[0m 
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[36m(train_cnn_ray_tune pid=3216755)[0m 
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[36m(train_cnn_ray_tune pid=3216747)[0m 
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[36m(train_cnn_ray_tune pid=3216747)[0m 
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[36m(train_cnn_ray_tune pid=3216740)[0m 
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[36m(train_cnn_ray_tune pid=3216743)[0m 
[1m  1/655[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m49s[0m 76ms/step - accuracy: 0.3750 - loss: 1.5154
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[36m(train_cnn_ray_tune pid=3216756)[0m 
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[36m(train_cnn_ray_tune pid=3216756)[0m 
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[36m(train_cnn_ray_tune pid=3216755)[0m 
[1m655/655[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m25s[0m 38ms/step - accuracy: 0.5079 - loss: 1.1146 - val_accuracy: 0.5060 - val_loss: 1.1212[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3216755)[0m Epoch 12/28[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3216755)[0m 
[1m  1/655[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:10[0m 107ms/step - accuracy: 0.5000 - loss: 1.2269[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3216755)[0m 
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[36m(train_cnn_ray_tune pid=3216743)[0m 
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[36m(train_cnn_ray_tune pid=3216756)[0m 
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[36m(train_cnn_ray_tune pid=3216755)[0m 
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[36m(train_cnn_ray_tune pid=3216740)[0m 
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[36m(train_cnn_ray_tune pid=3216743)[0m 
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[36m(train_cnn_ray_tune pid=3216752)[0m 
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[36m(train_cnn_ray_tune pid=3216756)[0m 
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[36m(train_cnn_ray_tune pid=3216755)[0m 
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[36m(train_cnn_ray_tune pid=3216755)[0m 
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[36m(train_cnn_ray_tune pid=3216752)[0m 
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[36m(train_cnn_ray_tune pid=3216740)[0m 
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[36m(train_cnn_ray_tune pid=3216747)[0m 
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[36m(train_cnn_ray_tune pid=3216756)[0m 
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[36m(train_cnn_ray_tune pid=3216756)[0m Epoch 15/19
[36m(train_cnn_ray_tune pid=3216756)[0m 
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Trial status: 14 TERMINATED | 6 RUNNING
Current time: 2025-11-07 16:46:59. Total running time: 10min 1s
Logical resource usage: 6.0/20 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     num_resblocks     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_987ea    RUNNING              3   adam            tanh                                   16                 64                  3                 1          0.000124674         27                                              │
│ trial_987ea    RUNNING              2   rmsprop         tanh                                   16                 64                  3                 1          8.8649e-06          23                                              │
│ trial_987ea    RUNNING              3   rmsprop         relu                                   16                 64                  5                 1          6.20968e-06         17                                              │
│ trial_987ea    RUNNING              3   adam            relu                                   16                 64                  5                 0          3.57777e-05         28                                              │
│ trial_987ea    RUNNING              3   rmsprop         tanh                                   16                 64                  3                 1          4.71571e-05         22                                              │
│ trial_987ea    RUNNING              3   rmsprop         relu                                   16                 64                  5                 1          0.000102887         19                                              │
│ trial_987ea    TERMINATED           2   adam            tanh                                   16                 32                  3                 0          1.76369e-05         22        1            395.874         0.473315 │
│ trial_987ea    TERMINATED           3   rmsprop         tanh                                   32                 16                  3                 1          0.0001207           28        1            393.491         0.497542 │
│ trial_987ea    TERMINATED           2   adam            tanh                                   32                 16                  3                 1          0.000103558         26        1            404.185         0.506671 │
│ trial_987ea    TERMINATED           2   rmsprop         tanh                                   32                 16                  5                 0          0.000131282         20        1            325.689         0.514396 │
│ trial_987ea    TERMINATED           2   rmsprop         relu                                   32                 32                  5                 0          4.30906e-05         29        1            288.299         0.505618 │
│ trial_987ea    TERMINATED           3   rmsprop         relu                                   32                 32                  3                 0          6.42207e-06         25        1            541.383         0.483497 │
│ trial_987ea    TERMINATED           2   rmsprop         tanh                                   32                 16                  3                 0          2.39155e-05         26        1            383.124         0.484902 │
│ trial_987ea    TERMINATED           3   rmsprop         tanh                                   32                 32                  3                 0          2.14512e-05         19        1            448.547         0.45927  │
│ trial_987ea    TERMINATED           2   adam            relu                                   32                 16                  3                 0          4.86659e-05         23        1            390.349         0.517205 │
│ trial_987ea    TERMINATED           3   adam            relu                                   16                 64                  5                 0          9.94182e-05         27        1            563.387         0.53125  │
│ trial_987ea    TERMINATED           2   adam            tanh                                   16                 16                  3                 0          0.000100097         16        1            448.52          0.484199 │
│ trial_987ea    TERMINATED           2   adam            relu                                   32                 64                  3                 1          1.89531e-05         28        1            510.209         0.537219 │
│ trial_987ea    TERMINATED           3   adam            tanh                                   32                 64                  5                 1          0.000197693         21        1            361.388         0.484199 │
│ trial_987ea    TERMINATED           2   rmsprop         relu                                   16                 16                  3                 0          4.74263e-05         18        1            456.392         0.523525 │
╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3216756)[0m 
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[36m(train_cnn_ray_tune pid=3216755)[0m 
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[36m(train_cnn_ray_tune pid=3216755)[0m 
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[36m(train_cnn_ray_tune pid=3216743)[0m 
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[36m(train_cnn_ray_tune pid=3216755)[0m 
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[36m(train_cnn_ray_tune pid=3216740)[0m 
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[36m(train_cnn_ray_tune pid=3216752)[0m 
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[36m(train_cnn_ray_tune pid=3216752)[0m Epoch 15/17[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3216752)[0m 
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[36m(train_cnn_ray_tune pid=3216752)[0m 
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[36m(train_cnn_ray_tune pid=3216752)[0m 
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[36m(train_cnn_ray_tune pid=3216747)[0m 
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[36m(train_cnn_ray_tune pid=3216747)[0m 
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[36m(train_cnn_ray_tune pid=3216740)[0m 
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[36m(train_cnn_ray_tune pid=3216755)[0m 
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[36m(train_cnn_ray_tune pid=3216755)[0m 
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[36m(train_cnn_ray_tune pid=3216740)[0m 
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[36m(train_cnn_ray_tune pid=3216740)[0m Epoch 15/27[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3216752)[0m 
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[36m(train_cnn_ray_tune pid=3216752)[0m 
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[36m(train_cnn_ray_tune pid=3216740)[0m 
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[36m(train_cnn_ray_tune pid=3216755)[0m 
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[36m(train_cnn_ray_tune pid=3216756)[0m 
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[36m(train_cnn_ray_tune pid=3216756)[0m 
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[36m(train_cnn_ray_tune pid=3216755)[0m 
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[36m(train_cnn_ray_tune pid=3216755)[0m Epoch 13/28
[36m(train_cnn_ray_tune pid=3216755)[0m 
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[36m(train_cnn_ray_tune pid=3216747)[0m 
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[36m(train_cnn_ray_tune pid=3216743)[0m 
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[36m(train_cnn_ray_tune pid=3216743)[0m Epoch 23/23
[36m(train_cnn_ray_tune pid=3216740)[0m 
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[36m(train_cnn_ray_tune pid=3216743)[0m 
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[36m(train_cnn_ray_tune pid=3216752)[0m 
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[36m(train_cnn_ray_tune pid=3216752)[0m 
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[36m(train_cnn_ray_tune pid=3216755)[0m 
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[36m(train_cnn_ray_tune pid=3216755)[0m 
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[36m(train_cnn_ray_tune pid=3216756)[0m 
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[36m(train_cnn_ray_tune pid=3216743)[0m 
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[36m(train_cnn_ray_tune pid=3216756)[0m 
[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m32s[0m 687ms/step
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[36m(train_cnn_ray_tune pid=3216756)[0m 
[1m 9/49[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 15ms/step
[36m(train_cnn_ray_tune pid=3216756)[0m 
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[36m(train_cnn_ray_tune pid=3216756)[0m 
[1m17/49[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 15ms/step
[36m(train_cnn_ray_tune pid=3216756)[0m 
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[36m(train_cnn_ray_tune pid=3216756)[0m 
[1m25/49[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 15ms/step
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[36m(train_cnn_ray_tune pid=3216756)[0m 
[1m33/49[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 15ms/step
[1m37/49[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 14ms/step
[36m(train_cnn_ray_tune pid=3216752)[0m 
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[36m(train_cnn_ray_tune pid=3216756)[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=3216756)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=3216740)[0m 
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[36m(train_cnn_ray_tune pid=3216756)[0m 
[1m40/49[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 15ms/step
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[36m(train_cnn_ray_tune pid=3216756)[0m 
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[36m(train_cnn_ray_tune pid=3216756)[0m 
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 26ms/step
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[36m(train_cnn_ray_tune pid=3216755)[0m 
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[36m(train_cnn_ray_tune pid=3216755)[0m 
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[36m(train_cnn_ray_tune pid=3216756)[0m 
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[36m(train_cnn_ray_tune pid=3216756)[0m 
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[36m(train_cnn_ray_tune pid=3216756)[0m 
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[36m(train_cnn_ray_tune pid=3216756)[0m 
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[36m(train_cnn_ray_tune pid=3216756)[0m 
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[36m(train_cnn_ray_tune pid=3216756)[0m 
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[36m(train_cnn_ray_tune pid=3216756)[0m 
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[1m78/89[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 9ms/step
[36m(train_cnn_ray_tune pid=3216756)[0m 
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[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 10ms/step

Trial trial_987ea finished iteration 1 at 2025-11-07 16:47:25. Total running time: 10min 27s
╭──────────────────────────────────────╮
│ Trial trial_987ea result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             624.635 │
│ time_total_s                 624.635 │
│ training_iteration                 1 │
│ val_accuracy                 0.49684 │
╰──────────────────────────────────────╯

Trial trial_987ea completed after 1 iterations at 2025-11-07 16:47:25. Total running time: 10min 27s
[36m(train_cnn_ray_tune pid=3216752)[0m Epoch 16/17
[36m(train_cnn_ray_tune pid=3216752)[0m 
[1m  1/655[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m40s[0m 61ms/step - accuracy: 0.3750 - loss: 1.1346
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[36m(train_cnn_ray_tune pid=3216747)[0m 
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[36m(train_cnn_ray_tune pid=3216740)[0m 
[1m  1/655[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:04[0m 98ms/step - accuracy: 0.5625 - loss: 0.8516
[36m(train_cnn_ray_tune pid=3216740)[0m 
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[36m(train_cnn_ray_tune pid=3216743)[0m 
[1m642/655[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 18ms/step - accuracy: 0.4261 - loss: 1.2676
[1m645/655[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 18ms/step - accuracy: 0.4261 - loss: 1.2676[32m [repeated 92x across cluster][0m
[36m(train_cnn_ray_tune pid=3216743)[0m 
[1m648/655[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 18ms/step - accuracy: 0.4260 - loss: 1.2676[32m [repeated 30x across cluster][0m

Trial status: 15 TERMINATED | 5 RUNNING
Current time: 2025-11-07 16:47:29. Total running time: 10min 31s
Logical resource usage: 5.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     num_resblocks     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_987ea    RUNNING              3   adam            tanh                                   16                 64                  3                 1          0.000124674         27                                              │
│ trial_987ea    RUNNING              2   rmsprop         tanh                                   16                 64                  3                 1          8.8649e-06          23                                              │
│ trial_987ea    RUNNING              3   rmsprop         relu                                   16                 64                  5                 1          6.20968e-06         17                                              │
│ trial_987ea    RUNNING              3   adam            relu                                   16                 64                  5                 0          3.57777e-05         28                                              │
│ trial_987ea    RUNNING              3   rmsprop         tanh                                   16                 64                  3                 1          4.71571e-05         22                                              │
│ trial_987ea    TERMINATED           2   adam            tanh                                   16                 32                  3                 0          1.76369e-05         22        1            395.874         0.473315 │
│ trial_987ea    TERMINATED           3   rmsprop         tanh                                   32                 16                  3                 1          0.0001207           28        1            393.491         0.497542 │
│ trial_987ea    TERMINATED           2   adam            tanh                                   32                 16                  3                 1          0.000103558         26        1            404.185         0.506671 │
│ trial_987ea    TERMINATED           2   rmsprop         tanh                                   32                 16                  5                 0          0.000131282         20        1            325.689         0.514396 │
│ trial_987ea    TERMINATED           2   rmsprop         relu                                   32                 32                  5                 0          4.30906e-05         29        1            288.299         0.505618 │
│ trial_987ea    TERMINATED           3   rmsprop         relu                                   32                 32                  3                 0          6.42207e-06         25        1            541.383         0.483497 │
│ trial_987ea    TERMINATED           2   rmsprop         tanh                                   32                 16                  3                 0          2.39155e-05         26        1            383.124         0.484902 │
│ trial_987ea    TERMINATED           3   rmsprop         tanh                                   32                 32                  3                 0          2.14512e-05         19        1            448.547         0.45927  │
│ trial_987ea    TERMINATED           2   adam            relu                                   32                 16                  3                 0          4.86659e-05         23        1            390.349         0.517205 │
│ trial_987ea    TERMINATED           3   adam            relu                                   16                 64                  5                 0          9.94182e-05         27        1            563.387         0.53125  │
│ trial_987ea    TERMINATED           2   adam            tanh                                   16                 16                  3                 0          0.000100097         16        1            448.52          0.484199 │
│ trial_987ea    TERMINATED           2   adam            relu                                   32                 64                  3                 1          1.89531e-05         28        1            510.209         0.537219 │
│ trial_987ea    TERMINATED           3   adam            tanh                                   32                 64                  5                 1          0.000197693         21        1            361.388         0.484199 │
│ trial_987ea    TERMINATED           3   rmsprop         relu                                   16                 64                  5                 1          0.000102887         19        1            624.635         0.49684  │
│ trial_987ea    TERMINATED           2   rmsprop         relu                                   16                 16                  3                 0          4.74263e-05         18        1            456.392         0.523525 │
╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3216752)[0m 
[1m138/655[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m12s[0m 25ms/step - accuracy: 0.4327 - loss: 1.3197
[1m140/655[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m12s[0m 25ms/step - accuracy: 0.4328 - loss: 1.3195[32m [repeated 46x across cluster][0m
[36m(train_cnn_ray_tune pid=3216740)[0m 
[1m 53/655[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m15s[0m 26ms/step - accuracy: 0.6053 - loss: 0.8900[32m [repeated 42x across cluster][0m
[36m(train_cnn_ray_tune pid=3216740)[0m Epoch 16/27[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3216743)[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=3216743)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=3216743)[0m 
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[1m 7/49[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 9ms/step   
[36m(train_cnn_ray_tune pid=3216747)[0m 
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[36m(train_cnn_ray_tune pid=3216743)[0m 
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[1m19/49[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 10ms/step
[36m(train_cnn_ray_tune pid=3216743)[0m 
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[1m31/49[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 9ms/step 
[36m(train_cnn_ray_tune pid=3216743)[0m 
[1m38/49[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 9ms/step
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[36m(train_cnn_ray_tune pid=3216743)[0m 
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[36m(train_cnn_ray_tune pid=3216743)[0m 
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 19ms/step
[36m(train_cnn_ray_tune pid=3216743)[0m 
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[36m(train_cnn_ray_tune pid=3216743)[0m 
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[36m(train_cnn_ray_tune pid=3216743)[0m 
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[36m(train_cnn_ray_tune pid=3216743)[0m 
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[36m(train_cnn_ray_tune pid=3216743)[0m 
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[36m(train_cnn_ray_tune pid=3216743)[0m 
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[36m(train_cnn_ray_tune pid=3216743)[0m 
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[36m(train_cnn_ray_tune pid=3216743)[0m 
[1m81/89[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 7ms/step
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 7ms/step

Trial trial_987ea finished iteration 1 at 2025-11-07 16:47:33. Total running time: 10min 35s
╭──────────────────────────────────────╮
│ Trial trial_987ea result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             632.097 │
│ time_total_s                 632.097 │
│ training_iteration                 1 │
│ val_accuracy                 0.48244 │
╰──────────────────────────────────────╯

Trial trial_987ea completed after 1 iterations at 2025-11-07 16:47:33. Total running time: 10min 35s
[36m(train_cnn_ray_tune pid=3216743)[0m 
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[36m(train_cnn_ray_tune pid=3216747)[0m 
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[36m(train_cnn_ray_tune pid=3216752)[0m 
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[36m(train_cnn_ray_tune pid=3216740)[0m 
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[36m(train_cnn_ray_tune pid=3216740)[0m 
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[36m(train_cnn_ray_tune pid=3216740)[0m 
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[36m(train_cnn_ray_tune pid=3216755)[0m 
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[36m(train_cnn_ray_tune pid=3216755)[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=3216755)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=3216755)[0m 
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[36m(train_cnn_ray_tune pid=3216755)[0m 
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[36m(train_cnn_ray_tune pid=3216755)[0m 
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[36m(train_cnn_ray_tune pid=3216755)[0m 
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[36m(train_cnn_ray_tune pid=3216755)[0m 
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Trial trial_987ea finished iteration 1 at 2025-11-07 16:47:37. Total running time: 10min 39s
╭──────────────────────────────────────╮
│ Trial trial_987ea result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             636.262 │
│ time_total_s                 636.262 │
│ training_iteration                 1 │
│ val_accuracy                 0.52914 │
╰──────────────────────────────────────╯

Trial trial_987ea completed after 1 iterations at 2025-11-07 16:47:37. Total running time: 10min 39s
[36m(train_cnn_ray_tune pid=3216755)[0m 
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[36m(train_cnn_ray_tune pid=3216752)[0m 
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[36m(train_cnn_ray_tune pid=3216740)[0m 
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[36m(train_cnn_ray_tune pid=3216740)[0m 
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[36m(train_cnn_ray_tune pid=3216747)[0m 
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[36m(train_cnn_ray_tune pid=3216747)[0m Epoch 18/22
[36m(train_cnn_ray_tune pid=3216747)[0m 
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[36m(train_cnn_ray_tune pid=3216747)[0m 
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[36m(train_cnn_ray_tune pid=3216747)[0m 
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[36m(train_cnn_ray_tune pid=3216747)[0m 
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[36m(train_cnn_ray_tune pid=3216755)[0m 
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[36m(train_cnn_ray_tune pid=3216740)[0m 
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[36m(train_cnn_ray_tune pid=3216740)[0m 
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[36m(train_cnn_ray_tune pid=3216740)[0m 
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[36m(train_cnn_ray_tune pid=3216740)[0m 
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[36m(train_cnn_ray_tune pid=3216740)[0m 
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[36m(train_cnn_ray_tune pid=3216740)[0m 
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[36m(train_cnn_ray_tune pid=3216740)[0m 
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[36m(train_cnn_ray_tune pid=3216752)[0m 
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[36m(train_cnn_ray_tune pid=3216740)[0m 
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[36m(train_cnn_ray_tune pid=3216740)[0m 
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[36m(train_cnn_ray_tune pid=3216740)[0m 
[1m655/655[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m14s[0m 21ms/step - accuracy: 0.6055 - loss: 0.9348 - val_accuracy: 0.4568 - val_loss: 1.2475[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3216740)[0m Epoch 17/27[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3216740)[0m 
[1m  1/655[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m47s[0m 72ms/step - accuracy: 0.6250 - loss: 0.6896[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3216740)[0m 
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[36m(train_cnn_ray_tune pid=3216752)[0m 
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[36m(train_cnn_ray_tune pid=3216752)[0m 
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[36m(train_cnn_ray_tune pid=3216747)[0m 
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[36m(train_cnn_ray_tune pid=3216747)[0m Epoch 19/22
[36m(train_cnn_ray_tune pid=3216747)[0m 
[1m  1/655[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m29s[0m 45ms/step - accuracy: 0.4375 - loss: 1.4029
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[36m(train_cnn_ray_tune pid=3216747)[0m 
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[36m(train_cnn_ray_tune pid=3216747)[0m 
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[36m(train_cnn_ray_tune pid=3216752)[0m 
[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m14s[0m 307ms/step
[36m(train_cnn_ray_tune pid=3216747)[0m 
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[36m(train_cnn_ray_tune pid=3216752)[0m 
[1m13/49[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 4ms/step   
[1m26/49[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 4ms/step
[36m(train_cnn_ray_tune pid=3216747)[0m 
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[36m(train_cnn_ray_tune pid=3216752)[0m 
[1m39/49[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 4ms/step
[36m(train_cnn_ray_tune pid=3216747)[0m 
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[36m(train_cnn_ray_tune pid=3216747)[0m 
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[36m(train_cnn_ray_tune pid=3216747)[0m 
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[36m(train_cnn_ray_tune pid=3216752)[0m 
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step
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[36m(train_cnn_ray_tune pid=3216747)[0m 
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[36m(train_cnn_ray_tune pid=3216747)[0m 
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[36m(train_cnn_ray_tune pid=3216747)[0m 
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[36m(train_cnn_ray_tune pid=3216747)[0m 
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Trial trial_987ea finished iteration 1 at 2025-11-07 16:47:53. Total running time: 10min 55s
[36m(train_cnn_ray_tune pid=3216752)[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=3216752)[0m   _log_deprecation_warning(
╭──────────────────────────────────────╮
│ Trial trial_987ea result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             652.771 │
│ time_total_s                 652.771 │
│ training_iteration                 1 │
│ val_accuracy                 0.47963 │
╰──────────────────────────────────────╯

Trial trial_987ea completed after 1 iterations at 2025-11-07 16:47:53. Total running time: 10min 55s
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[36m(train_cnn_ray_tune pid=3216747)[0m 
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[36m(train_cnn_ray_tune pid=3216747)[0m 
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[36m(train_cnn_ray_tune pid=3216747)[0m 
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[36m(train_cnn_ray_tune pid=3216747)[0m 
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[36m(train_cnn_ray_tune pid=3216747)[0m 
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[36m(train_cnn_ray_tune pid=3216747)[0m 
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[36m(train_cnn_ray_tune pid=3216747)[0m 
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[36m(train_cnn_ray_tune pid=3216747)[0m 
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[36m(train_cnn_ray_tune pid=3216747)[0m 
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[36m(train_cnn_ray_tune pid=3216747)[0m 
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[36m(train_cnn_ray_tune pid=3216747)[0m 
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[36m(train_cnn_ray_tune pid=3216747)[0m 
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[36m(train_cnn_ray_tune pid=3216747)[0m 
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[36m(train_cnn_ray_tune pid=3216740)[0m 
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[36m(train_cnn_ray_tune pid=3216740)[0m Epoch 18/27
[36m(train_cnn_ray_tune pid=3216747)[0m 
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[36m(train_cnn_ray_tune pid=3216747)[0m 
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[36m(train_cnn_ray_tune pid=3216747)[0m 
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[36m(train_cnn_ray_tune pid=3216747)[0m 
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[36m(train_cnn_ray_tune pid=3216747)[0m 
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[36m(train_cnn_ray_tune pid=3216747)[0m 
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[36m(train_cnn_ray_tune pid=3216747)[0m 
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[36m(train_cnn_ray_tune pid=3216747)[0m 
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[36m(train_cnn_ray_tune pid=3216740)[0m 
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[1m518/655[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m1s[0m 9ms/step - accuracy: 0.6113 - loss: 0.9251
[36m(train_cnn_ray_tune pid=3216740)[0m 
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[1m534/655[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m1s[0m 9ms/step - accuracy: 0.6112 - loss: 0.9252
[36m(train_cnn_ray_tune pid=3216747)[0m 
[1m655/655[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 10ms/step - accuracy: 0.5612 - loss: 1.0350 - val_accuracy: 0.4814 - val_loss: 1.1874
[36m(train_cnn_ray_tune pid=3216747)[0m Epoch 20/22
[36m(train_cnn_ray_tune pid=3216740)[0m 
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[36m(train_cnn_ray_tune pid=3216747)[0m 
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[36m(train_cnn_ray_tune pid=3216740)[0m 
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[1m564/655[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 9ms/step - accuracy: 0.6110 - loss: 0.9254
[36m(train_cnn_ray_tune pid=3216740)[0m 
[1m653/655[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 9ms/step - accuracy: 0.6107 - loss: 0.9258
[36m(train_cnn_ray_tune pid=3216740)[0m 
[1m486/655[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m1s[0m 10ms/step - accuracy: 0.6115 - loss: 0.9249
[1m494/655[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m1s[0m 10ms/step - accuracy: 0.6115 - loss: 0.9249[32m [repeated 28x across cluster][0m
[36m(train_cnn_ray_tune pid=3216740)[0m 
[1m502/655[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m1s[0m 9ms/step - accuracy: 0.6114 - loss: 0.9250 [32m [repeated 10x across cluster][0m

Trial status: 18 TERMINATED | 2 RUNNING
Current time: 2025-11-07 16:47:59. Total running time: 11min 1s
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     num_resblocks     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_987ea    RUNNING              3   adam            tanh                                   16                 64                  3                 1          0.000124674         27                                              │
│ trial_987ea    RUNNING              3   rmsprop         tanh                                   16                 64                  3                 1          4.71571e-05         22                                              │
│ trial_987ea    TERMINATED           2   adam            tanh                                   16                 32                  3                 0          1.76369e-05         22        1            395.874         0.473315 │
│ trial_987ea    TERMINATED           3   rmsprop         tanh                                   32                 16                  3                 1          0.0001207           28        1            393.491         0.497542 │
│ trial_987ea    TERMINATED           2   rmsprop         tanh                                   16                 64                  3                 1          8.8649e-06          23        1            632.097         0.482444 │
│ trial_987ea    TERMINATED           2   adam            tanh                                   32                 16                  3                 1          0.000103558         26        1            404.185         0.506671 │
│ trial_987ea    TERMINATED           2   rmsprop         tanh                                   32                 16                  5                 0          0.000131282         20        1            325.689         0.514396 │
│ trial_987ea    TERMINATED           2   rmsprop         relu                                   32                 32                  5                 0          4.30906e-05         29        1            288.299         0.505618 │
│ trial_987ea    TERMINATED           3   rmsprop         relu                                   32                 32                  3                 0          6.42207e-06         25        1            541.383         0.483497 │
│ trial_987ea    TERMINATED           3   rmsprop         relu                                   16                 64                  5                 1          6.20968e-06         17        1            652.771         0.479635 │
│ trial_987ea    TERMINATED           2   rmsprop         tanh                                   32                 16                  3                 0          2.39155e-05         26        1            383.124         0.484902 │
│ trial_987ea    TERMINATED           3   rmsprop         tanh                                   32                 32                  3                 0          2.14512e-05         19        1            448.547         0.45927  │
│ trial_987ea    TERMINATED           3   adam            relu                                   16                 64                  5                 0          3.57777e-05         28        1            636.262         0.529143 │
│ trial_987ea    TERMINATED           2   adam            relu                                   32                 16                  3                 0          4.86659e-05         23        1            390.349         0.517205 │
│ trial_987ea    TERMINATED           3   adam            relu                                   16                 64                  5                 0          9.94182e-05         27        1            563.387         0.53125  │
│ trial_987ea    TERMINATED           2   adam            tanh                                   16                 16                  3                 0          0.000100097         16        1            448.52          0.484199 │
│ trial_987ea    TERMINATED           2   adam            relu                                   32                 64                  3                 1          1.89531e-05         28        1            510.209         0.537219 │
│ trial_987ea    TERMINATED           3   adam            tanh                                   32                 64                  5                 1          0.000197693         21        1            361.388         0.484199 │
│ trial_987ea    TERMINATED           3   rmsprop         relu                                   16                 64                  5                 1          0.000102887         19        1            624.635         0.49684  │
│ trial_987ea    TERMINATED           2   rmsprop         relu                                   16                 16                  3                 0          4.74263e-05         18        1            456.392         0.523525 │
╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3216740)[0m 
[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 270ms/step
[36m(train_cnn_ray_tune pid=3216740)[0m 
[1m17/49[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step   
[1m33/49[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 3ms/step
[36m(train_cnn_ray_tune pid=3216740)[0m 
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 8ms/step
[36m(train_cnn_ray_tune pid=3216740)[0m 
[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 23ms/step
[36m(train_cnn_ray_tune pid=3216740)[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=3216740)[0m   _log_deprecation_warning(
2025-11-07 16:48:06,596	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_CAPTURE24_acc_superclasses_CPA_METs/CAPTURE24_hyperparameters_tuning' in 0.0053s.
[36m(train_cnn_ray_tune pid=3216740)[0m 
[1m18/89[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step 
[1m35/89[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step
[36m(train_cnn_ray_tune pid=3216740)[0m 
[1m53/89[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step
[1m70/89[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 3ms/step

Trial trial_987ea finished iteration 1 at 2025-11-07 16:48:00. Total running time: 11min 2s
╭──────────────────────────────────────╮
│ Trial trial_987ea result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             659.556 │
│ time_total_s                 659.556 │
│ training_iteration                 1 │
│ val_accuracy                 0.47542 │
╰──────────────────────────────────────╯

Trial trial_987ea completed after 1 iterations at 2025-11-07 16:48:00. Total running time: 11min 2s
[36m(train_cnn_ray_tune pid=3216740)[0m 
[1m88/89[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 3ms/step
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step
[36m(train_cnn_ray_tune pid=3216747)[0m Epoch 21/22
[36m(train_cnn_ray_tune pid=3216747)[0m 
[1m  1/655[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m14s[0m 22ms/step - accuracy: 0.3125 - loss: 1.7940
[1m 12/655[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 5ms/step - accuracy: 0.5051 - loss: 1.2163  
[36m(train_cnn_ray_tune pid=3216747)[0m 
[1m 64/655[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 5ms/step - accuracy: 0.5317 - loss: 1.0744
[1m 74/655[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 5ms/step - accuracy: 0.5324 - loss: 1.0703[32m [repeated 47x across cluster][0m
[36m(train_cnn_ray_tune pid=3216747)[0m 
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[36m(train_cnn_ray_tune pid=3216747)[0m 
[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 156ms/step
[1m25/49[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step  
[36m(train_cnn_ray_tune pid=3216747)[0m 
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[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step
[36m(train_cnn_ray_tune pid=3216747)[0m 
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[1m27/89[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step 
[36m(train_cnn_ray_tune pid=3216747)[0m 
[1m55/89[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step
[1m83/89[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step

Trial trial_987ea finished iteration 1 at 2025-11-07 16:48:06. Total running time: 11min 8s
╭──────────────────────────────────────╮
│ Trial trial_987ea result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             665.467 │
│ time_total_s                 665.467 │
│ training_iteration                 1 │
│ val_accuracy                 0.50702 │
╰──────────────────────────────────────╯

Trial trial_987ea completed after 1 iterations at 2025-11-07 16:48:06. Total running time: 11min 8s

Trial status: 20 TERMINATED
Current time: 2025-11-07 16:48:06. Total running time: 11min 8s
Logical resource usage: 1.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
I0000 00:00:1762530486.723151 3215107 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13760 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
[36m(train_cnn_ray_tune pid=3216747)[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=3216747)[0m   _log_deprecation_warning(
╭────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     num_resblocks     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_987ea    TERMINATED           2   adam            tanh                                   16                 32                  3                 0          1.76369e-05         22        1            395.874         0.473315 │
│ trial_987ea    TERMINATED           3   adam            tanh                                   16                 64                  3                 1          0.000124674         27        1            659.556         0.475421 │
│ trial_987ea    TERMINATED           3   rmsprop         tanh                                   32                 16                  3                 1          0.0001207           28        1            393.491         0.497542 │
│ trial_987ea    TERMINATED           2   rmsprop         tanh                                   16                 64                  3                 1          8.8649e-06          23        1            632.097         0.482444 │
│ trial_987ea    TERMINATED           2   adam            tanh                                   32                 16                  3                 1          0.000103558         26        1            404.185         0.506671 │
│ trial_987ea    TERMINATED           2   rmsprop         tanh                                   32                 16                  5                 0          0.000131282         20        1            325.689         0.514396 │
│ trial_987ea    TERMINATED           2   rmsprop         relu                                   32                 32                  5                 0          4.30906e-05         29        1            288.299         0.505618 │
│ trial_987ea    TERMINATED           3   rmsprop         relu                                   32                 32                  3                 0          6.42207e-06         25        1            541.383         0.483497 │
│ trial_987ea    TERMINATED           3   rmsprop         relu                                   16                 64                  5                 1          6.20968e-06         17        1            652.771         0.479635 │
│ trial_987ea    TERMINATED           2   rmsprop         tanh                                   32                 16                  3                 0          2.39155e-05         26        1            383.124         0.484902 │
│ trial_987ea    TERMINATED           3   rmsprop         tanh                                   32                 32                  3                 0          2.14512e-05         19        1            448.547         0.45927  │
│ trial_987ea    TERMINATED           3   adam            relu                                   16                 64                  5                 0          3.57777e-05         28        1            636.262         0.529143 │
│ trial_987ea    TERMINATED           2   adam            relu                                   32                 16                  3                 0          4.86659e-05         23        1            390.349         0.517205 │
│ trial_987ea    TERMINATED           3   rmsprop         tanh                                   16                 64                  3                 1          4.71571e-05         22        1            665.467         0.507022 │
│ trial_987ea    TERMINATED           3   adam            relu                                   16                 64                  5                 0          9.94182e-05         27        1            563.387         0.53125  │
│ trial_987ea    TERMINATED           2   adam            tanh                                   16                 16                  3                 0          0.000100097         16        1            448.52          0.484199 │
│ trial_987ea    TERMINATED           2   adam            relu                                   32                 64                  3                 1          1.89531e-05         28        1            510.209         0.537219 │
│ trial_987ea    TERMINATED           3   adam            tanh                                   32                 64                  5                 1          0.000197693         21        1            361.388         0.484199 │
│ trial_987ea    TERMINATED           3   rmsprop         relu                                   16                 64                  5                 1          0.000102887         19        1            624.635         0.49684  │
│ trial_987ea    TERMINATED           2   rmsprop         relu                                   16                 16                  3                 0          4.74263e-05         18        1            456.392         0.523525 │
╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

Mejores hiperparámetros: {'N_capas': 2, 'optimizador': 'adam', 'funcion_activacion': 'relu', 'tamanho_minilote': 32, 'numero_filtros': 64, 'tamanho_filtro': 3, 'num_resblocks': 1, 'tasa_aprendizaje': 1.895305519972482e-05, 'epochs': 28}
Epoch 1/28
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762530489.293111 3261731 service.cc:152] XLA service 0x7c42f8006700 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762530489.293166 3261731 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-07 16:48:09.348669: 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 00:00:1762530489.664053 3261731 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762530491.965846 3261731 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 2/28

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Epoch 3/28

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Epoch 4/28

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[1m 30/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4032 - loss: 1.4828 
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[1m 92/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4015 - loss: 1.4623
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[1m243/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4050 - loss: 1.4374
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[1m307/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4061 - loss: 1.4320
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Epoch 5/28

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[1m 31/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4058 - loss: 1.3676 
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[1m310/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4158 - loss: 1.3809
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Epoch 6/28

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[1m 29/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4692 - loss: 1.3336 
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[1m180/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4397 - loss: 1.3368
[1m209/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4383 - loss: 1.3379
[1m238/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4371 - loss: 1.3383
[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4362 - loss: 1.3389
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Epoch 7/28

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[1m 32/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4331 - loss: 1.3381 
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[1m 96/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4350 - loss: 1.3432
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Epoch 8/28

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[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4464 - loss: 1.2983
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Epoch 9/28

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[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4703 - loss: 1.2329
[1m302/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4697 - loss: 1.2349
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Epoch 10/28

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[1m 85/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4417 - loss: 1.2828
[1m117/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4446 - loss: 1.2776
[1m149/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4479 - loss: 1.2721
[1m176/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4500 - loss: 1.2683
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[1m235/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4529 - loss: 1.2630
[1m267/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4543 - loss: 1.2604
[1m299/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4554 - loss: 1.2582
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4563 - loss: 1.2566 - val_accuracy: 0.5197 - val_loss: 1.2304
Epoch 11/28

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[1m 33/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4836 - loss: 1.1937 
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[1m 97/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4692 - loss: 1.2233
[1m129/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4689 - loss: 1.2249
[1m162/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4694 - loss: 1.2251
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[1m222/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4696 - loss: 1.2242
[1m252/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4699 - loss: 1.2234
[1m283/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4699 - loss: 1.2232
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Epoch 12/28

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[1m 31/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4788 - loss: 1.2148 
[1m 60/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4717 - loss: 1.2217
[1m 90/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4709 - loss: 1.2193
[1m122/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4712 - loss: 1.2171
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[1m182/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4724 - loss: 1.2133
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[1m242/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4730 - loss: 1.2112
[1m271/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4730 - loss: 1.2111
[1m302/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4729 - loss: 1.2108
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Epoch 13/28

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[1m 28/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5236 - loss: 1.1492 
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[1m 92/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5031 - loss: 1.1697
[1m119/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4999 - loss: 1.1734
[1m148/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4972 - loss: 1.1766
[1m176/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4954 - loss: 1.1790
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[1m239/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4920 - loss: 1.1837
[1m271/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4909 - loss: 1.1849
[1m301/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4902 - loss: 1.1854
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4896 - loss: 1.1858 - val_accuracy: 0.5281 - val_loss: 1.2036
Epoch 14/28

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[1m 30/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4996 - loss: 1.1850 
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[1m 92/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4838 - loss: 1.1989
[1m123/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4834 - loss: 1.1979
[1m153/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4828 - loss: 1.1972
[1m181/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4823 - loss: 1.1970
[1m211/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4816 - loss: 1.1966
[1m241/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4810 - loss: 1.1966
[1m271/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4806 - loss: 1.1965
[1m299/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4805 - loss: 1.1962
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4803 - loss: 1.1960 - val_accuracy: 0.5176 - val_loss: 1.1982
Epoch 15/28

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[1m 29/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5121 - loss: 1.1317 
[1m 58/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4997 - loss: 1.1541
[1m 90/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4968 - loss: 1.1627
[1m120/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4950 - loss: 1.1662
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[1m183/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4920 - loss: 1.1695
[1m216/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4919 - loss: 1.1696
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[1m278/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4907 - loss: 1.1718
[1m308/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4906 - loss: 1.1722
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Epoch 16/28

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[1m 30/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5061 - loss: 1.1610 
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Epoch 17/28

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[1m271/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4931 - loss: 1.1625
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Epoch 18/28

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[1m183/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4982 - loss: 1.1467
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[1m247/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4984 - loss: 1.1483
[1m277/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4987 - loss: 1.1482
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[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4993 - loss: 1.1479 - val_accuracy: 0.5151 - val_loss: 1.1888
Epoch 19/28

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[1m 33/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4908 - loss: 1.1588 
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[1m 92/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4960 - loss: 1.1549
[1m125/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4964 - loss: 1.1554
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[1m185/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4959 - loss: 1.1550
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Epoch 20/28

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[1m 32/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5433 - loss: 1.0606 
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Epoch 21/28

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[1m268/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5176 - loss: 1.1161
[1m301/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5170 - loss: 1.1171
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Epoch 22/28

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[1m120/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5035 - loss: 1.1246
[1m151/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5035 - loss: 1.1264
[1m180/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5038 - loss: 1.1273
[1m210/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5039 - loss: 1.1282
[1m240/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5044 - loss: 1.1284
[1m268/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5052 - loss: 1.1281
[1m299/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5062 - loss: 1.1275
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5070 - loss: 1.1268 - val_accuracy: 0.5235 - val_loss: 1.1856
Epoch 23/28

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5938 - loss: 1.1466
[1m 31/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5237 - loss: 1.1005 
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[1m 93/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5230 - loss: 1.1074
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[1m177/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5241 - loss: 1.1032
[1m209/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5241 - loss: 1.1017
[1m240/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5242 - loss: 1.1004
[1m271/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5242 - loss: 1.0995
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Epoch 24/28

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[1m 29/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5280 - loss: 1.1333 
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Epoch 25/28

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Epoch 26/28

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[1m181/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5174 - loss: 1.0843
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[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5194 - loss: 1.0882 - val_accuracy: 0.5197 - val_loss: 1.1697
Epoch 27/28

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[1m 29/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5447 - loss: 1.0738 
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Epoch 28/28

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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m24s[0m 504ms/step2025-11-07 16:48:36.892996: 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_141', 8 bytes spill stores, 8 bytes spill loads

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Saved model to disk.
[36m(train_cnn_ray_tune pid=3216747)[0m 
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[1m655/655[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step - accuracy: 0.5607 - loss: 1.0174[32m [repeated 28x across cluster][0m
[36m(train_cnn_ray_tune pid=3216747)[0m 
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2025-11-07 16:48:50.119033: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 16:48:50.130480: 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 00:00:1762530530.143548 3265319 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762530530.147672 3265319 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762530530.157704 3265319 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762530530.157722 3265319 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762530530.157724 3265319 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762530530.157726 3265319 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 16:48:50.160876: 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.
I0000 00:00:1762530532.366956 3265319 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13760 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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Epoch 1/28
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762530534.837953 3265430 service.cc:152] XLA service 0x73d038013340 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762530534.837991 3265430 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-07 16:48:54.888473: 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 00:00:1762530535.203260 3265430 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762530537.532755 3265430 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m24:39[0m 5s/step - accuracy: 0.2500 - loss: 2.1494
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Epoch 2/28

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Epoch 3/28

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[1m272/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3886 - loss: 1.5229
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Epoch 4/28

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[1m181/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4088 - loss: 1.4838
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[1m273/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4086 - loss: 1.4794
[1m303/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4087 - loss: 1.4777
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4088 - loss: 1.4764 - val_accuracy: 0.4371 - val_loss: 1.2899
Epoch 5/28

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[1m 29/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4295 - loss: 1.4207 
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[1m124/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4154 - loss: 1.4441
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[1m187/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4139 - loss: 1.4437
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Epoch 6/28

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[1m 27/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4114 - loss: 1.4304 
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Epoch 7/28

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Epoch 8/28

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[1m276/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4570 - loss: 1.2994
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Epoch 9/28

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[1m 29/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4322 - loss: 1.2970 
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[1m 90/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4398 - loss: 1.3062
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[1m184/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4427 - loss: 1.3128
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Epoch 10/28

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Epoch 11/28

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[1m271/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4657 - loss: 1.2640
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Epoch 12/28

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[1m297/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4656 - loss: 1.2648
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4657 - loss: 1.2640
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4657 - loss: 1.2640 - val_accuracy: 0.4775 - val_loss: 1.2063
Epoch 13/28

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[1m 30/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4653 - loss: 1.2410 
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[1m118/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4681 - loss: 1.2358
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[1m182/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4695 - loss: 1.2323
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[1m308/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4703 - loss: 1.2304
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Epoch 14/28

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[1m 28/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4820 - loss: 1.1925 
[1m 57/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4752 - loss: 1.2082
[1m 88/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4780 - loss: 1.2057
[1m120/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4792 - loss: 1.2045
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[1m180/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4789 - loss: 1.2065
[1m211/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4783 - loss: 1.2080
[1m238/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4773 - loss: 1.2097
[1m268/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4762 - loss: 1.2114
[1m299/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4752 - loss: 1.2129
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4745 - loss: 1.2140 - val_accuracy: 0.4863 - val_loss: 1.1889
Epoch 15/28

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[1m 30/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4950 - loss: 1.1780 
[1m 61/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4915 - loss: 1.1849
[1m 91/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4860 - loss: 1.1924
[1m121/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4839 - loss: 1.1952
[1m150/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4831 - loss: 1.1964
[1m179/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4820 - loss: 1.1975
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[1m233/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4806 - loss: 1.1991
[1m264/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4799 - loss: 1.2005
[1m294/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4794 - loss: 1.2014
[1m326/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.4791 - loss: 1.2020
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4791 - loss: 1.2021 - val_accuracy: 0.4937 - val_loss: 1.1793
Epoch 16/28

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 25ms/step - accuracy: 0.5000 - loss: 1.2760
[1m 30/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4477 - loss: 1.2428 
[1m 62/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4623 - loss: 1.2160
[1m 90/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4690 - loss: 1.2029
[1m121/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4737 - loss: 1.1966
[1m151/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4757 - loss: 1.1952
[1m179/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4764 - loss: 1.1953
[1m209/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4774 - loss: 1.1947
[1m242/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4783 - loss: 1.1940
[1m269/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4787 - loss: 1.1937
[1m301/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4791 - loss: 1.1936
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4792 - loss: 1.1938 - val_accuracy: 0.4933 - val_loss: 1.1777
Epoch 17/28

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.5312 - loss: 1.0608
[1m 32/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4685 - loss: 1.1622 
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[1m 84/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4717 - loss: 1.1705
[1m111/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4721 - loss: 1.1747
[1m137/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4723 - loss: 1.1770
[1m165/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4726 - loss: 1.1789
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[1m289/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4741 - loss: 1.1819
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Epoch 18/28

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[1m279/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4927 - loss: 1.1790
[1m310/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4929 - loss: 1.1781
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Epoch 19/28

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Epoch 20/28

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[1m 28/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4904 - loss: 1.2109 
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[1m 89/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5022 - loss: 1.1812
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[1m149/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5017 - loss: 1.1751
[1m181/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5008 - loss: 1.1733
[1m210/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5005 - loss: 1.1714
[1m242/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5002 - loss: 1.1698
[1m271/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4998 - loss: 1.1687
[1m300/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4995 - loss: 1.1679
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4993 - loss: 1.1674 - val_accuracy: 0.5070 - val_loss: 1.1580
Epoch 21/28

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[1m 29/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5000 - loss: 1.1420 
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[1m 90/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5008 - loss: 1.1486
[1m119/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5002 - loss: 1.1510
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[1m181/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4984 - loss: 1.1539
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[1m241/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4981 - loss: 1.1552
[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4984 - loss: 1.1554
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Epoch 22/28

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[1m 31/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5192 - loss: 1.1170 
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[1m 91/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5174 - loss: 1.1132
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[1m182/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5114 - loss: 1.1203
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[1m305/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5079 - loss: 1.1260
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Epoch 23/28

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[1m180/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4971 - loss: 1.1548
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[1m271/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4974 - loss: 1.1538
[1m301/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4976 - loss: 1.1533
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Epoch 24/28

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[1m 32/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5091 - loss: 1.1520 
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[1m118/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5173 - loss: 1.1265
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[1m173/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5178 - loss: 1.1236
[1m203/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5175 - loss: 1.1232
[1m234/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5169 - loss: 1.1232
[1m265/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5162 - loss: 1.1233
[1m298/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5155 - loss: 1.1235
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5149 - loss: 1.1239 - val_accuracy: 0.5056 - val_loss: 1.1467
Epoch 25/28

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 23ms/step - accuracy: 0.3750 - loss: 1.3983
[1m 30/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4928 - loss: 1.1769 
[1m 62/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5036 - loss: 1.1506
[1m 93/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5074 - loss: 1.1435
[1m125/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5082 - loss: 1.1402
[1m155/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5081 - loss: 1.1381
[1m181/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5085 - loss: 1.1365
[1m211/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5090 - loss: 1.1347
[1m239/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5097 - loss: 1.1328
[1m271/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5102 - loss: 1.1310
[1m302/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5103 - loss: 1.1298
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5104 - loss: 1.1291 - val_accuracy: 0.5235 - val_loss: 1.1304
Epoch 26/28

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Epoch 27/28

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Epoch 28/28

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[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5165 - loss: 1.1090 - val_accuracy: 0.5207 - val_loss: 1.1363

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m24s[0m 514ms/step2025-11-07 16:49:22.607083: 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_141', 8 bytes spill stores, 8 bytes spill loads


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[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 23ms/step
Saved model to disk.

=== EJECUCIÓN 1 ===

--- TRAIN (ejecución 1) ---

--- TEST (ejecución 1) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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Loaded model from disk.
(1545, 3, 250)
(10469, 3, 250)

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[1m174/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 872us/step
[1m229/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 884us/step
[1m286/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 885us/step
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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 16ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 19ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 19ms/step

[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 15ms/step
[1m58/89[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 881us/step
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step  
Global accuracy score (validation) = 50.95 [%]
Global F1 score (validation) = 45.44 [%]
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[[0.2414989  0.30557835 0.4407376  0.01218515]
 [0.0849088  0.29635245 0.34808102 0.27065775]
 [0.18230893 0.12826246 0.56028223 0.12914638]
 ...
 [0.25231802 0.17411703 0.5181613  0.05540355]
 [0.19949177 0.21357086 0.47114238 0.11579503]
 [0.22502384 0.2348335  0.49820432 0.04193828]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 57.15 [%]
Global accuracy score (test) = 52.56 [%]
Global F1 score (train) = 53.95 [%]
Global F1 score (test) = 48.41 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.47      0.10      0.17       400
MODERATE-INTENSITY       0.47      0.74      0.57       400
         SEDENTARY       0.53      0.77      0.63       400
VIGOROUS-INTENSITY       0.67      0.49      0.57       345

          accuracy                           0.53      1545
         macro avg       0.54      0.52      0.48      1545
      weighted avg       0.53      0.53      0.48      1545


Accuracy capturado en la ejecución 1: 52.56 [%]
F1-score capturado en la ejecución 1: 48.41 [%]

=== EJECUCIÓN 2 ===

--- TRAIN (ejecución 2) ---

--- TEST (ejecución 2) ---
2025-11-07 16:49:33.610634: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 16:49:33.621979: 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 00:00:1762530573.635594 3268985 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762530573.639631 3268985 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762530573.650013 3268985 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762530573.650033 3268985 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762530573.650035 3268985 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762530573.650037 3268985 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 16:49:33.653069: 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.
I0000 00:00:1762530575.898216 3268985 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13760 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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Epoch 1/28
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762530578.374739 3269116 service.cc:152] XLA service 0x7c01dc015750 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762530578.374785 3269116 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-07 16:49:38.432207: 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 00:00:1762530578.757332 3269116 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762530581.099774 3269116 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m24:50[0m 5s/step - accuracy: 0.0938 - loss: 1.9736
[1m 26/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2391 - loss: 1.7763  
[1m 57/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2581 - loss: 1.7327
[1m 87/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2665 - loss: 1.7167
[1m118/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2748 - loss: 1.7041
[1m150/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2835 - loss: 1.6904
[1m181/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2901 - loss: 1.6798
[1m214/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2960 - loss: 1.6699
[1m241/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3004 - loss: 1.6625
[1m271/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3047 - loss: 1.6552
[1m301/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.3085 - loss: 1.6487
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step - accuracy: 0.3117 - loss: 1.6432
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m9s[0m 13ms/step - accuracy: 0.3118 - loss: 1.6430 - val_accuracy: 0.4164 - val_loss: 1.3119
Epoch 2/28

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[1m 29/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3838 - loss: 1.5280 
[1m 59/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3855 - loss: 1.5166
[1m 91/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3860 - loss: 1.5169
[1m123/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3879 - loss: 1.5139
[1m155/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3898 - loss: 1.5093
[1m183/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3907 - loss: 1.5066
[1m214/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3916 - loss: 1.5047
[1m243/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3920 - loss: 1.5027
[1m272/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3923 - loss: 1.5005
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Epoch 3/28

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Epoch 4/28

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Epoch 5/28

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Epoch 6/28

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[1m 27/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4594 - loss: 1.2656 
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Epoch 7/28

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[1m 30/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4497 - loss: 1.2980 
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[1m241/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4510 - loss: 1.2845
[1m272/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4502 - loss: 1.2855
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Epoch 8/28

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[1m 29/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4823 - loss: 1.2331 
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[1m209/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4635 - loss: 1.2562
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[1m269/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4601 - loss: 1.2590
[1m298/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4588 - loss: 1.2599
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Epoch 9/28

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[1m 32/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4358 - loss: 1.2834 
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[1m122/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4532 - loss: 1.2703
[1m151/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4538 - loss: 1.2706
[1m182/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4541 - loss: 1.2709
[1m212/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4548 - loss: 1.2703
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[1m274/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4556 - loss: 1.2692
[1m303/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4558 - loss: 1.2686
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Epoch 10/28

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[1m 30/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5076 - loss: 1.1681 
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[1m 90/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4951 - loss: 1.1973
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[1m275/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4838 - loss: 1.2169
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Epoch 11/28

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[1m 30/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4712 - loss: 1.1807 
[1m 54/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4773 - loss: 1.1771
[1m 85/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4755 - loss: 1.1865
[1m111/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4749 - loss: 1.1922
[1m141/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4750 - loss: 1.1961
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[1m203/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4756 - loss: 1.1999
[1m233/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4759 - loss: 1.2010
[1m265/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4760 - loss: 1.2022
[1m295/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4760 - loss: 1.2029
[1m325/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.4760 - loss: 1.2036
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4760 - loss: 1.2037 - val_accuracy: 0.5137 - val_loss: 1.1802
Epoch 12/28

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[1m 30/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4591 - loss: 1.2463 
[1m 60/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4591 - loss: 1.2372
[1m 88/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4584 - loss: 1.2341
[1m117/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4583 - loss: 1.2321
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[1m176/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4598 - loss: 1.2295
[1m205/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4610 - loss: 1.2278
[1m232/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4622 - loss: 1.2259
[1m261/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4632 - loss: 1.2244
[1m290/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4643 - loss: 1.2230
[1m318/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.4652 - loss: 1.2214
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4656 - loss: 1.2210 - val_accuracy: 0.5116 - val_loss: 1.1683
Epoch 13/28

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[1m 28/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4640 - loss: 1.1905 
[1m 58/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4685 - loss: 1.1825
[1m 91/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4679 - loss: 1.1903
[1m123/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4671 - loss: 1.1957
[1m154/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4670 - loss: 1.1983
[1m183/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4670 - loss: 1.2003
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[1m244/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4688 - loss: 1.2007
[1m274/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4700 - loss: 1.1999
[1m304/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4709 - loss: 1.1991
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4715 - loss: 1.1985 - val_accuracy: 0.5147 - val_loss: 1.1613
Epoch 14/28

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[1m 33/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4527 - loss: 1.2060 
[1m 63/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4641 - loss: 1.1931
[1m 92/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4681 - loss: 1.1912
[1m123/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4734 - loss: 1.1858
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[1m187/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4781 - loss: 1.1820
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[1m279/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4793 - loss: 1.1849
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Epoch 15/28

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Epoch 16/28

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Epoch 17/28

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Epoch 18/28

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[1m323/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.4995 - loss: 1.1547
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4995 - loss: 1.1547 - val_accuracy: 0.5158 - val_loss: 1.1559
Epoch 19/28

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4375 - loss: 1.2040
[1m 28/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4993 - loss: 1.1484 
[1m 58/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4990 - loss: 1.1416
[1m 89/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4969 - loss: 1.1432
[1m119/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4969 - loss: 1.1442
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[1m180/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4994 - loss: 1.1416
[1m210/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5006 - loss: 1.1403
[1m241/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5014 - loss: 1.1390
[1m271/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5020 - loss: 1.1380
[1m301/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5023 - loss: 1.1374
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5024 - loss: 1.1373 - val_accuracy: 0.5302 - val_loss: 1.1450
Epoch 20/28

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 23ms/step - accuracy: 0.5938 - loss: 0.9136
[1m 30/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5258 - loss: 1.0728 
[1m 60/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5168 - loss: 1.0918
[1m 90/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5114 - loss: 1.1046
[1m117/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5094 - loss: 1.1099
[1m147/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5083 - loss: 1.1147
[1m176/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5068 - loss: 1.1198
[1m207/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5052 - loss: 1.1240
[1m240/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5040 - loss: 1.1267
[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5034 - loss: 1.1282
[1m300/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5029 - loss: 1.1297
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5025 - loss: 1.1306 - val_accuracy: 0.5218 - val_loss: 1.1449
Epoch 21/28

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4688 - loss: 1.0119
[1m 31/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5008 - loss: 1.1084 
[1m 60/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5023 - loss: 1.1207
[1m 92/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5026 - loss: 1.1281
[1m123/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5035 - loss: 1.1304
[1m153/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5041 - loss: 1.1316
[1m180/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5046 - loss: 1.1322
[1m210/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5047 - loss: 1.1333
[1m239/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5052 - loss: 1.1335
[1m271/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5054 - loss: 1.1337
[1m302/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5054 - loss: 1.1343
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5054 - loss: 1.1347 - val_accuracy: 0.5256 - val_loss: 1.1378
Epoch 22/28

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.4062 - loss: 1.2259
[1m 30/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4979 - loss: 1.1383 
[1m 59/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5030 - loss: 1.1323
[1m 88/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5049 - loss: 1.1314
[1m117/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5063 - loss: 1.1292
[1m147/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5071 - loss: 1.1270
[1m177/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5080 - loss: 1.1254
[1m209/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5089 - loss: 1.1243
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[1m299/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5095 - loss: 1.1250
[1m327/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.5096 - loss: 1.1252
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Epoch 23/28

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4375 - loss: 1.1736
[1m 32/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4791 - loss: 1.1739 
[1m 64/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4854 - loss: 1.1590
[1m 94/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4890 - loss: 1.1522
[1m125/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4920 - loss: 1.1479
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[1m267/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4998 - loss: 1.1365
[1m295/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5007 - loss: 1.1350
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Epoch 24/28

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.6250 - loss: 1.1077
[1m 30/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5611 - loss: 1.0791 
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[1m266/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5193 - loss: 1.1157
[1m296/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5189 - loss: 1.1160
[1m327/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.5186 - loss: 1.1162
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5185 - loss: 1.1162 - val_accuracy: 0.5355 - val_loss: 1.1250
Epoch 25/28

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5625 - loss: 0.9748
[1m 23/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5220 - loss: 1.0697 
[1m 54/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5247 - loss: 1.0823
[1m 83/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5217 - loss: 1.0867
[1m114/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5213 - loss: 1.0888
[1m143/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5209 - loss: 1.0912
[1m174/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5200 - loss: 1.0935
[1m204/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5192 - loss: 1.0948
[1m233/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5188 - loss: 1.0956
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[1m292/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5182 - loss: 1.0970
[1m323/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.5179 - loss: 1.0977
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5178 - loss: 1.0979 - val_accuracy: 0.5298 - val_loss: 1.1378
Epoch 26/28

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.6250 - loss: 0.8919
[1m 31/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5276 - loss: 1.0787 
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[1m 91/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5250 - loss: 1.0857
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Epoch 27/28

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Epoch 28/28

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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m23s[0m 487ms/step2025-11-07 16:50:06.066423: 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_141', 8 bytes spill stores, 8 bytes spill loads

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Saved model to disk.
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
Loaded model from disk.
(1545, 3, 250)
(10469, 3, 250)

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[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 15ms/step
[1m59/89[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 869us/step
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 988us/step
Global accuracy score (validation) = 52.49 [%]
Global F1 score (validation) = 50.98 [%]
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[[0.27048123 0.09004388 0.54333    0.09614491]
 [0.27834857 0.34885895 0.15086229 0.2219302 ]
 [0.32682848 0.21525137 0.44322246 0.01469776]
 ...
 [0.29196247 0.11829448 0.51199377 0.07774927]
 [0.32645872 0.1036316  0.4620339  0.10787574]
 [0.24005951 0.10809453 0.5836369  0.06820906]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 55.35 [%]
Global accuracy score (test) = 48.22 [%]
Global F1 score (train) = 54.38 [%]
Global F1 score (test) = 46.91 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.39      0.34      0.36       400
MODERATE-INTENSITY       0.53      0.32      0.40       400
         SEDENTARY       0.47      0.80      0.59       400
VIGOROUS-INTENSITY       0.60      0.46      0.52       345

          accuracy                           0.48      1545
         macro avg       0.50      0.48      0.47      1545
      weighted avg       0.49      0.48      0.47      1545


Accuracy capturado en la ejecución 2: 48.22 [%]
F1-score capturado en la ejecución 2: 46.91 [%]

=== EJECUCIÓN 3 ===

--- TRAIN (ejecución 3) ---

--- TEST (ejecución 3) ---
2025-11-07 16:50:16.932149: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 16:50:16.943645: 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 00:00:1762530616.956810 3272660 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762530616.961212 3272660 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762530616.971232 3272660 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762530616.971258 3272660 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762530616.971260 3272660 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762530616.971261 3272660 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 16:50:16.974511: 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.
I0000 00:00:1762530619.236195 3272660 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13760 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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Epoch 1/28
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762530621.745672 3272771 service.cc:152] XLA service 0x7df458014ac0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762530621.745702 3272771 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-07 16:50:21.795371: 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 00:00:1762530622.108314 3272771 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762530624.433674 3272771 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m24:49[0m 5s/step - accuracy: 0.2188 - loss: 1.9139
[1m 27/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2337 - loss: 1.9815  
[1m 57/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2477 - loss: 1.9595
[1m 88/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2590 - loss: 1.9285
[1m119/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2689 - loss: 1.8998
[1m151/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2774 - loss: 1.8778
[1m181/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2838 - loss: 1.8608
[1m211/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2892 - loss: 1.8461
[1m242/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2943 - loss: 1.8319
[1m272/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2987 - loss: 1.8193
[1m304/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.3027 - loss: 1.8074
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step - accuracy: 0.3053 - loss: 1.7995
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m9s[0m 13ms/step - accuracy: 0.3054 - loss: 1.7992 - val_accuracy: 0.4414 - val_loss: 1.3026
Epoch 2/28

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[1m 63/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3747 - loss: 1.5468
[1m 96/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3739 - loss: 1.5494
[1m127/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3737 - loss: 1.5496
[1m157/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3737 - loss: 1.5487
[1m190/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3740 - loss: 1.5470
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Epoch 3/28

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Epoch 4/28

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[1m291/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4044 - loss: 1.4326
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Epoch 5/28

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[1m186/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4115 - loss: 1.3965
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[1m242/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4140 - loss: 1.3927
[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4146 - loss: 1.3915
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[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4160 - loss: 1.3892 - val_accuracy: 0.4961 - val_loss: 1.1950
Epoch 6/28

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[1m 26/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4252 - loss: 1.3658 
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[1m 86/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4226 - loss: 1.3690
[1m115/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4225 - loss: 1.3700
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[1m179/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4236 - loss: 1.3650
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Epoch 7/28

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[1m 29/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4294 - loss: 1.3701 
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Epoch 8/28

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[1m281/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4434 - loss: 1.3078
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Epoch 9/28

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[1m148/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4549 - loss: 1.2676
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[1m234/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4542 - loss: 1.2681
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[1m292/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4533 - loss: 1.2694
[1m323/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.4528 - loss: 1.2700
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4527 - loss: 1.2701 - val_accuracy: 0.5133 - val_loss: 1.1650
Epoch 10/28

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[1m 27/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4752 - loss: 1.2478 
[1m 58/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4708 - loss: 1.2618
[1m 89/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4694 - loss: 1.2625
[1m120/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4660 - loss: 1.2649
[1m148/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4639 - loss: 1.2652
[1m178/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4632 - loss: 1.2639
[1m209/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4629 - loss: 1.2629
[1m238/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4626 - loss: 1.2624
[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4623 - loss: 1.2619
[1m300/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4622 - loss: 1.2612
[1m327/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.4622 - loss: 1.2607
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Epoch 11/28

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[1m 29/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4672 - loss: 1.2342 
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[1m272/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4636 - loss: 1.2456
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Epoch 12/28

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[1m182/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4747 - loss: 1.2318
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[1m247/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4730 - loss: 1.2328
[1m276/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4725 - loss: 1.2330
[1m306/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4723 - loss: 1.2326
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Epoch 13/28

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[1m 30/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4476 - loss: 1.2373 
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[1m153/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4668 - loss: 1.2117
[1m181/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4688 - loss: 1.2100
[1m210/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4706 - loss: 1.2086
[1m237/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4719 - loss: 1.2075
[1m268/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4729 - loss: 1.2068
[1m300/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4735 - loss: 1.2065
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4738 - loss: 1.2064 - val_accuracy: 0.5246 - val_loss: 1.1525
Epoch 14/28

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[1m 32/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4898 - loss: 1.1728 
[1m 62/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4819 - loss: 1.1915
[1m 91/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4790 - loss: 1.1966
[1m119/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4781 - loss: 1.1989
[1m149/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4774 - loss: 1.1995
[1m180/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4766 - loss: 1.2005
[1m208/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4764 - loss: 1.2012
[1m239/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4763 - loss: 1.2021
[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4764 - loss: 1.2027
[1m302/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4766 - loss: 1.2030
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Epoch 15/28

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.4375 - loss: 1.2784
[1m 32/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4753 - loss: 1.2021 
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Epoch 16/28

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Epoch 17/28

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[1m306/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4912 - loss: 1.1730
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Epoch 18/28

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Epoch 19/28

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[1m 89/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4870 - loss: 1.1558
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[1m185/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4913 - loss: 1.1524
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[1m246/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4941 - loss: 1.1497
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[1m306/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4960 - loss: 1.1476
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Epoch 20/28

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[1m179/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4939 - loss: 1.1482
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[1m242/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4927 - loss: 1.1469
[1m273/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4925 - loss: 1.1460
[1m302/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4927 - loss: 1.1451
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Epoch 21/28

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[1m 32/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4928 - loss: 1.1670 
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[1m 92/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4966 - loss: 1.1503
[1m122/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4978 - loss: 1.1463
[1m154/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4996 - loss: 1.1427
[1m183/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5006 - loss: 1.1408
[1m212/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5008 - loss: 1.1400
[1m243/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5007 - loss: 1.1398
[1m271/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5009 - loss: 1.1398
[1m302/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5012 - loss: 1.1395
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5015 - loss: 1.1395 - val_accuracy: 0.5320 - val_loss: 1.1412
Epoch 22/28

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[1m 30/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5523 - loss: 1.0739 
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[1m 93/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5340 - loss: 1.0874
[1m125/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5274 - loss: 1.0935
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[1m186/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5193 - loss: 1.1052
[1m216/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5175 - loss: 1.1088
[1m248/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5162 - loss: 1.1114
[1m278/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5151 - loss: 1.1134
[1m309/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5143 - loss: 1.1151
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Epoch 23/28

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 23ms/step - accuracy: 0.4688 - loss: 1.1020
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Epoch 24/28

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Epoch 25/28

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[1m296/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5285 - loss: 1.1004
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[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5275 - loss: 1.1013 - val_accuracy: 0.5327 - val_loss: 1.1334
Epoch 26/28

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[1m290/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5157 - loss: 1.1031
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[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5160 - loss: 1.1039 - val_accuracy: 0.5351 - val_loss: 1.1248
Epoch 27/28

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5938 - loss: 0.9370
[1m 33/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5165 - loss: 1.1068 
[1m 64/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5168 - loss: 1.1144
[1m 97/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5169 - loss: 1.1119
[1m126/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5170 - loss: 1.1094
[1m157/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5168 - loss: 1.1075
[1m186/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5170 - loss: 1.1058
[1m218/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5172 - loss: 1.1048
[1m248/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5173 - loss: 1.1045
[1m282/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5177 - loss: 1.1040
[1m313/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.5180 - loss: 1.1034
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5182 - loss: 1.1032 - val_accuracy: 0.5404 - val_loss: 1.1266
Epoch 28/28

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4375 - loss: 1.1178
[1m 29/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5110 - loss: 1.1481 
[1m 62/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5206 - loss: 1.1312
[1m 96/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5205 - loss: 1.1223
[1m126/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5203 - loss: 1.1190
[1m156/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5196 - loss: 1.1180
[1m187/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5197 - loss: 1.1160
[1m218/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5207 - loss: 1.1133
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[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5234 - loss: 1.1065 - val_accuracy: 0.5312 - val_loss: 1.1362

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m23s[0m 496ms/step2025-11-07 16:50:49.352245: 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_141', 8 bytes spill stores, 8 bytes spill loads


[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 21ms/step  
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 21ms/step
Saved model to disk.
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Loaded model from disk.
(1545, 3, 250)
(10469, 3, 250)

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6:59[0m 1s/step
[1m 54/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 954us/step
[1m113/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 899us/step
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[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m3s[0m 4ms/step

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 16ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 19ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 19ms/step

[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 15ms/step
[1m57/89[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 900us/step
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step  
Global accuracy score (validation) = 53.34 [%]
Global F1 score (validation) = 51.42 [%]
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[[0.16508122 0.07450472 0.64766717 0.11274685]
 [0.29011235 0.4049551  0.1223414  0.18259118]
 [0.23445414 0.11556655 0.52112854 0.12885082]
 ...
 [0.20319447 0.16670087 0.53242147 0.09768321]
 [0.22365361 0.12478349 0.5099512  0.14161168]
 [0.16587213 0.1334782  0.634306   0.06634367]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 56.54 [%]
Global accuracy score (test) = 49.97 [%]
Global F1 score (train) = 55.83 [%]
Global F1 score (test) = 48.33 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.41      0.23      0.30       400
MODERATE-INTENSITY       0.51      0.52      0.51       400
         SEDENTARY       0.47      0.79      0.59       400
VIGOROUS-INTENSITY       0.67      0.45      0.54       345

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


Accuracy capturado en la ejecución 3: 49.97 [%]
F1-score capturado en la ejecución 3: 48.33 [%]

=== EJECUCIÓN 4 ===

--- TRAIN (ejecución 4) ---

--- TEST (ejecución 4) ---
2025-11-07 16:51:00.280715: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 16:51:00.292253: 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 00:00:1762530660.306446 3276309 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762530660.310851 3276309 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762530660.321283 3276309 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762530660.321305 3276309 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762530660.321307 3276309 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762530660.321309 3276309 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 16:51:00.324634: 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.
I0000 00:00:1762530662.537788 3276309 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13760 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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Epoch 1/28
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762530665.017783 3276441 service.cc:152] XLA service 0x7bd278028800 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762530665.017826 3276441 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-07 16:51:05.067215: 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 00:00:1762530665.381461 3276441 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762530667.693788 3276441 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m24:38[0m 5s/step - accuracy: 0.1562 - loss: 1.8277
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[1m122/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2539 - loss: 1.7985
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[1m187/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2677 - loss: 1.7677
[1m220/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2729 - loss: 1.7547
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[1m283/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2818 - loss: 1.7328
[1m317/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.2860 - loss: 1.7224
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[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m9s[0m 13ms/step - accuracy: 0.2875 - loss: 1.7190 - val_accuracy: 0.4470 - val_loss: 1.2403
Epoch 2/28

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[1m 87/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3550 - loss: 1.5979
[1m117/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3599 - loss: 1.5809
[1m147/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3643 - loss: 1.5674
[1m179/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3675 - loss: 1.5563
[1m210/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3695 - loss: 1.5483
[1m242/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3714 - loss: 1.5412
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Epoch 3/28

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[1m181/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4051 - loss: 1.4385
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Epoch 4/28

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Epoch 5/28

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Epoch 6/28

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[1m186/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4443 - loss: 1.3187
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[1m247/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4444 - loss: 1.3136
[1m277/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4444 - loss: 1.3117
[1m307/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4443 - loss: 1.3101
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Epoch 7/28

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[1m 27/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4752 - loss: 1.2303 
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[1m 90/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4600 - loss: 1.2807
[1m119/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4579 - loss: 1.2843
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Epoch 8/28

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Epoch 9/28

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[1m278/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4511 - loss: 1.2642
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Epoch 10/28

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[1m 31/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4651 - loss: 1.2152 
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[1m120/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4553 - loss: 1.2398
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[1m179/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4581 - loss: 1.2363
[1m210/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4591 - loss: 1.2350
[1m240/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4598 - loss: 1.2341
[1m273/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4604 - loss: 1.2338
[1m304/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4609 - loss: 1.2337
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4613 - loss: 1.2337 - val_accuracy: 0.5133 - val_loss: 1.1232
Epoch 11/28

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 23ms/step - accuracy: 0.5938 - loss: 0.9697
[1m 30/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5181 - loss: 1.1604 
[1m 57/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5107 - loss: 1.1784
[1m 88/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5033 - loss: 1.1932
[1m118/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4981 - loss: 1.2011
[1m153/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4939 - loss: 1.2063
[1m182/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4916 - loss: 1.2085
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[1m245/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4873 - loss: 1.2124
[1m276/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4860 - loss: 1.2131
[1m307/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4849 - loss: 1.2138
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Epoch 12/28

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[1m 28/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4660 - loss: 1.2107 
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[1m 89/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4667 - loss: 1.2074
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[1m208/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4728 - loss: 1.1958
[1m239/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4738 - loss: 1.1955
[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4746 - loss: 1.1954
[1m302/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4754 - loss: 1.1949
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Epoch 13/28

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[1m246/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4826 - loss: 1.1677
[1m277/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4826 - loss: 1.1693
[1m306/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4824 - loss: 1.1707
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Epoch 14/28

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[1m 32/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4715 - loss: 1.2233 
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[1m 96/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4731 - loss: 1.2070
[1m125/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4742 - loss: 1.2029
[1m157/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4755 - loss: 1.2000
[1m185/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4766 - loss: 1.1979
[1m217/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4778 - loss: 1.1959
[1m246/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4784 - loss: 1.1950
[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4789 - loss: 1.1944
[1m300/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4796 - loss: 1.1934
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4803 - loss: 1.1925 - val_accuracy: 0.5260 - val_loss: 1.1028
Epoch 15/28

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[1m 28/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4802 - loss: 1.2175 
[1m 59/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4911 - loss: 1.1979
[1m 91/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4934 - loss: 1.1858
[1m121/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4946 - loss: 1.1771
[1m151/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4964 - loss: 1.1711
[1m182/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4978 - loss: 1.1668
[1m212/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4980 - loss: 1.1652
[1m237/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4979 - loss: 1.1646
[1m268/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4979 - loss: 1.1638
[1m298/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4979 - loss: 1.1630
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Epoch 16/28

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Epoch 17/28

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Epoch 18/28

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Epoch 19/28

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[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5123 - loss: 1.1265 - val_accuracy: 0.5334 - val_loss: 1.0900
Epoch 20/28

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[1m 32/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5047 - loss: 1.1767 
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[1m 94/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5178 - loss: 1.1361
[1m126/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5191 - loss: 1.1292
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[1m187/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5172 - loss: 1.1272
[1m215/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5165 - loss: 1.1272
[1m244/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5161 - loss: 1.1271
[1m276/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5157 - loss: 1.1269
[1m306/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5152 - loss: 1.1273
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5148 - loss: 1.1275 - val_accuracy: 0.5362 - val_loss: 1.0908
Epoch 21/28

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4688 - loss: 1.2068
[1m 32/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5102 - loss: 1.1331 
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[1m 95/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5185 - loss: 1.1254
[1m126/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5186 - loss: 1.1237
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[1m185/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5173 - loss: 1.1227
[1m216/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5167 - loss: 1.1220
[1m247/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5159 - loss: 1.1219
[1m279/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5150 - loss: 1.1218
[1m310/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5143 - loss: 1.1219
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5140 - loss: 1.1218 - val_accuracy: 0.5446 - val_loss: 1.0844
Epoch 22/28

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4375 - loss: 0.9727
[1m 30/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5282 - loss: 1.0779 
[1m 62/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5249 - loss: 1.0891
[1m 92/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5209 - loss: 1.0960
[1m121/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5210 - loss: 1.0977
[1m151/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5213 - loss: 1.0986
[1m180/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5215 - loss: 1.0989
[1m209/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5220 - loss: 1.0985
[1m239/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5222 - loss: 1.0985
[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5218 - loss: 1.0995
[1m301/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5215 - loss: 1.1003
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5213 - loss: 1.1007 - val_accuracy: 0.5327 - val_loss: 1.0885
Epoch 23/28

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5938 - loss: 1.0704
[1m 27/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5147 - loss: 1.1140 
[1m 57/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5199 - loss: 1.1047
[1m 85/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5188 - loss: 1.1065
[1m117/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5179 - loss: 1.1079
[1m146/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5178 - loss: 1.1086
[1m177/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5184 - loss: 1.1078
[1m208/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5189 - loss: 1.1068
[1m240/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5195 - loss: 1.1057
[1m271/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5200 - loss: 1.1047
[1m302/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5203 - loss: 1.1038
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Epoch 24/28

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Epoch 25/28

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Epoch 26/28

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[1m187/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5282 - loss: 1.0842
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Epoch 27/28

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.6875 - loss: 0.8303
[1m 25/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5479 - loss: 1.0479 
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Epoch 28/28

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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m23s[0m 491ms/step2025-11-07 16:51:32.524896: 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_141', 8 bytes spill stores, 8 bytes spill loads

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Saved model to disk.
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Loaded model from disk.
(1545, 3, 250)
(10469, 3, 250)

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7:00[0m 1s/step
[1m 56/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 915us/step
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[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 15ms/step
[1m64/89[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 804us/step
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 935us/step
Global accuracy score (validation) = 53.55 [%]
Global F1 score (validation) = 51.28 [%]
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[[0.38655633 0.25645366 0.34863088 0.00835919]
 [0.18843354 0.07472759 0.60673237 0.13010652]
 [0.02775417 0.02835501 0.9266941  0.01719671]
 ...
 [0.2284286  0.1103764  0.59597355 0.06522152]
 [0.26156944 0.12571071 0.51745015 0.09526968]
 [0.22767594 0.17558885 0.5479023  0.04883288]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 58.8 [%]
Global accuracy score (test) = 50.74 [%]
Global F1 score (train) = 58.09 [%]
Global F1 score (test) = 49.0 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.41      0.21      0.28       400
MODERATE-INTENSITY       0.49      0.60      0.54       400
         SEDENTARY       0.50      0.72      0.59       400
VIGOROUS-INTENSITY       0.62      0.50      0.55       345

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


Accuracy capturado en la ejecución 4: 50.74 [%]
F1-score capturado en la ejecución 4: 49.0 [%]

=== EJECUCIÓN 5 ===

--- TRAIN (ejecución 5) ---

--- TEST (ejecución 5) ---
2025-11-07 16:51:43.646089: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 16:51:43.657443: 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 00:00:1762530703.671067 3279983 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762530703.675410 3279983 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762530703.685473 3279983 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762530703.685499 3279983 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762530703.685502 3279983 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762530703.685503 3279983 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 16:51:43.688829: 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.
I0000 00:00:1762530705.919764 3279983 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13760 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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Epoch 1/28
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762530708.441117 3280115 service.cc:152] XLA service 0x744994017e30 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762530708.441149 3280115 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-07 16:51:48.490370: 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 00:00:1762530708.802932 3280115 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762530711.113623 3280115 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 2/28

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Epoch 3/28

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Epoch 4/28

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Epoch 5/28

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Epoch 6/28

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Epoch 7/28

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[1m182/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4378 - loss: 1.3743
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[1m271/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4361 - loss: 1.3682
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Epoch 8/28

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[1m 31/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4960 - loss: 1.2407 
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Epoch 9/28

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Epoch 10/28

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[1m242/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4578 - loss: 1.2748
[1m274/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4566 - loss: 1.2777
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Epoch 11/28

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[1m 28/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4635 - loss: 1.2076 
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[1m120/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4627 - loss: 1.2492
[1m147/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4613 - loss: 1.2539
[1m176/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4602 - loss: 1.2572
[1m207/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4592 - loss: 1.2602
[1m238/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4586 - loss: 1.2625
[1m268/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4582 - loss: 1.2640
[1m300/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4580 - loss: 1.2651
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4580 - loss: 1.2656 - val_accuracy: 0.5081 - val_loss: 1.1555
Epoch 12/28

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4375 - loss: 1.3600
[1m 29/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4731 - loss: 1.2555 
[1m 56/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4666 - loss: 1.2673
[1m 87/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4630 - loss: 1.2690
[1m118/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4633 - loss: 1.2645
[1m149/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4635 - loss: 1.2627
[1m179/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4633 - loss: 1.2618
[1m210/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4631 - loss: 1.2608
[1m240/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4631 - loss: 1.2597
[1m272/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4633 - loss: 1.2584
[1m304/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4637 - loss: 1.2570
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4639 - loss: 1.2561 - val_accuracy: 0.5102 - val_loss: 1.1520
Epoch 13/28

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[1m 33/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4535 - loss: 1.2433 
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Epoch 14/28

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[1m280/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4827 - loss: 1.2087
[1m311/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4823 - loss: 1.2094
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Epoch 15/28

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[1m191/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4878 - loss: 1.1968
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[1m285/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4866 - loss: 1.1982
[1m317/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.4863 - loss: 1.1986
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4862 - loss: 1.1987 - val_accuracy: 0.5270 - val_loss: 1.1344
Epoch 16/28

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[1m 30/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4942 - loss: 1.1739 
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[1m 90/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4932 - loss: 1.1901
[1m121/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4937 - loss: 1.1903
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[1m180/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4937 - loss: 1.1908
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[1m271/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4929 - loss: 1.1906
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Epoch 17/28

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[1m 28/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4350 - loss: 1.2605 
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Epoch 18/28

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[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5116 - loss: 1.1565
[1m301/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5110 - loss: 1.1566
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Epoch 19/28

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[1m127/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5011 - loss: 1.1468
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[1m187/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4985 - loss: 1.1540
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[1m249/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4980 - loss: 1.1565
[1m279/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4979 - loss: 1.1570
[1m307/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4977 - loss: 1.1575
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4977 - loss: 1.1578 - val_accuracy: 0.5186 - val_loss: 1.1434
Epoch 20/28

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[1m 29/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4980 - loss: 1.1512 
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[1m 90/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4992 - loss: 1.1496
[1m124/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4994 - loss: 1.1475
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[1m187/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4999 - loss: 1.1461
[1m218/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5001 - loss: 1.1461
[1m248/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5002 - loss: 1.1467
[1m279/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5001 - loss: 1.1473
[1m306/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4999 - loss: 1.1478
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Epoch 21/28

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[1m 32/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5011 - loss: 1.1985 
[1m 64/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4972 - loss: 1.1800
[1m 95/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4982 - loss: 1.1675
[1m124/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4994 - loss: 1.1615
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[1m218/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5023 - loss: 1.1532
[1m249/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5027 - loss: 1.1521
[1m278/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5029 - loss: 1.1511
[1m308/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5031 - loss: 1.1502
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Epoch 22/28

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[1m 29/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4807 - loss: 1.1786 
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[1m 92/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4836 - loss: 1.1673
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[1m183/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4907 - loss: 1.1601
[1m217/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4926 - loss: 1.1574
[1m247/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4937 - loss: 1.1559
[1m278/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4941 - loss: 1.1550
[1m308/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4946 - loss: 1.1540
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4950 - loss: 1.1533 - val_accuracy: 0.5348 - val_loss: 1.1296
Epoch 23/28

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[1m 30/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5103 - loss: 1.1510 
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[1m 91/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5026 - loss: 1.1612
[1m119/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5034 - loss: 1.1566
[1m150/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5051 - loss: 1.1512
[1m179/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5059 - loss: 1.1484
[1m210/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5063 - loss: 1.1470
[1m238/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5065 - loss: 1.1462
[1m268/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5070 - loss: 1.1450
[1m298/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5074 - loss: 1.1439
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5077 - loss: 1.1430 - val_accuracy: 0.5214 - val_loss: 1.1344
Epoch 24/28

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4375 - loss: 1.1673
[1m 30/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4886 - loss: 1.1689 
[1m 63/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4997 - loss: 1.1489
[1m 96/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5030 - loss: 1.1390
[1m127/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5059 - loss: 1.1314
[1m157/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5079 - loss: 1.1271
[1m184/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5086 - loss: 1.1258
[1m215/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5089 - loss: 1.1252
[1m245/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5094 - loss: 1.1244
[1m275/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5100 - loss: 1.1234
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Epoch 25/28

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[1m 32/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4838 - loss: 1.2005 
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Epoch 26/28

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Epoch 27/28

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[1m293/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5083 - loss: 1.1341
[1m323/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.5089 - loss: 1.1326
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Epoch 28/28

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[1m149/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5256 - loss: 1.0917
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[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5232 - loss: 1.0961
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[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5224 - loss: 1.0976 - val_accuracy: 0.5274 - val_loss: 1.1265

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m22s[0m 479ms/step2025-11-07 16:52:16.022378: 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_141', 8 bytes spill stores, 8 bytes spill loads

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Saved model to disk.
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Loaded model from disk.
(1545, 3, 250)
(10469, 3, 250)

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7:08[0m 1s/step
[1m 53/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 965us/step
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[1m163/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 929us/step
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[1m278/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 907us/step
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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 16ms/step
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[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 19ms/step

[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 17ms/step
[1m59/89[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 872us/step
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 978us/step
Global accuracy score (validation) = 52.6 [%]
Global F1 score (validation) = 51.91 [%]
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[[0.26580733 0.19925253 0.44202802 0.09291214]
 [0.24552949 0.22212955 0.4546949  0.07764604]
 [0.14159858 0.07026891 0.67969304 0.10843946]
 ...
 [0.26478416 0.11618546 0.55909526 0.05993512]
 [0.26102155 0.12856436 0.52458054 0.08583353]
 [0.21294934 0.13744727 0.60632926 0.04327418]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 58.32 [%]
Global accuracy score (test) = 50.42 [%]
Global F1 score (train) = 58.08 [%]
Global F1 score (test) = 49.8 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.41      0.29      0.34       400
MODERATE-INTENSITY       0.48      0.60      0.53       400
         SEDENTARY       0.49      0.68      0.57       400
VIGOROUS-INTENSITY       0.75      0.43      0.55       345

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


Accuracy capturado en la ejecución 5: 50.42 [%]
F1-score capturado en la ejecución 5: 49.8 [%]

=== EJECUCIÓN 6 ===

--- TRAIN (ejecución 6) ---

--- TEST (ejecución 6) ---
2025-11-07 16:52:26.956158: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 16:52:26.967896: 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 00:00:1762530746.981356 3283635 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762530746.985411 3283635 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762530746.995420 3283635 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762530746.995439 3283635 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762530746.995441 3283635 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762530746.995442 3283635 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 16:52:26.998611: 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.
I0000 00:00:1762530749.205348 3283635 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13760 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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Epoch 1/28
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762530751.692179 3283767 service.cc:152] XLA service 0x781110015a10 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762530751.692222 3283767 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-07 16:52:31.746360: 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 00:00:1762530752.072659 3283767 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762530754.405397 3283767 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m24:51[0m 5s/step - accuracy: 0.1562 - loss: 2.0917
[1m 29/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2079 - loss: 1.9115  
[1m 62/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2372 - loss: 1.8621
[1m 94/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2525 - loss: 1.8319
[1m125/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2611 - loss: 1.8141
[1m156/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2676 - loss: 1.7996
[1m186/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2724 - loss: 1.7875
[1m216/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2769 - loss: 1.7760
[1m245/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2810 - loss: 1.7654
[1m276/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2851 - loss: 1.7547
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Epoch 2/28

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Epoch 3/28

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[1m314/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.4028 - loss: 1.4333
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Epoch 4/28

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[1m185/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4224 - loss: 1.3746
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Epoch 5/28

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[1m 31/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4190 - loss: 1.3401 
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[1m 94/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4196 - loss: 1.3532
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[1m183/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4222 - loss: 1.3586
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Epoch 6/28

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Epoch 7/28

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[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4381 - loss: 1.3175
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Epoch 8/28

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[1m181/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4556 - loss: 1.2920
[1m209/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4556 - loss: 1.2907
[1m239/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4552 - loss: 1.2901
[1m269/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4548 - loss: 1.2898
[1m300/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4548 - loss: 1.2893
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4547 - loss: 1.2888 - val_accuracy: 0.5249 - val_loss: 1.1603
Epoch 9/28

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[1m 90/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4361 - loss: 1.3448
[1m119/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4397 - loss: 1.3310
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[1m180/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4452 - loss: 1.3115
[1m211/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4466 - loss: 1.3053
[1m240/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4475 - loss: 1.3011
[1m274/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4487 - loss: 1.2970
[1m302/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4497 - loss: 1.2939
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Epoch 10/28

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[1m 31/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4906 - loss: 1.1853 
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[1m287/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4765 - loss: 1.2228
[1m317/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.4757 - loss: 1.2243
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Epoch 11/28

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[1m272/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4514 - loss: 1.2446
[1m302/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4525 - loss: 1.2430
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Epoch 12/28

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[1m117/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4739 - loss: 1.2112
[1m149/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4749 - loss: 1.2084
[1m179/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4749 - loss: 1.2077
[1m210/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4748 - loss: 1.2070
[1m242/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4746 - loss: 1.2067
[1m273/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4744 - loss: 1.2065
[1m304/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4742 - loss: 1.2066
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4741 - loss: 1.2065 - val_accuracy: 0.5274 - val_loss: 1.1494
Epoch 13/28

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4375 - loss: 1.1774
[1m 33/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4744 - loss: 1.2092 
[1m 67/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4750 - loss: 1.2115
[1m 96/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4744 - loss: 1.2133
[1m127/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4737 - loss: 1.2143
[1m157/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4735 - loss: 1.2143
[1m187/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4735 - loss: 1.2137
[1m217/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4735 - loss: 1.2133
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[1m278/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4737 - loss: 1.2121
[1m309/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4737 - loss: 1.2119
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4737 - loss: 1.2118 - val_accuracy: 0.5270 - val_loss: 1.1427
Epoch 14/28

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[1m 30/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4988 - loss: 1.1594 
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Epoch 15/28

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Epoch 16/28

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[1m182/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4931 - loss: 1.1815
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Epoch 17/28

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Epoch 18/28

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[1m187/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4941 - loss: 1.1455
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[1m246/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4950 - loss: 1.1478
[1m275/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4952 - loss: 1.1490
[1m306/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4956 - loss: 1.1497
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Epoch 19/28

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[1m184/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4999 - loss: 1.1417
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[1m245/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4986 - loss: 1.1455
[1m273/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4982 - loss: 1.1462
[1m305/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4980 - loss: 1.1466
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Epoch 20/28

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[1m 30/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5359 - loss: 1.0731 
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[1m125/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5191 - loss: 1.0989
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[1m186/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5162 - loss: 1.1058
[1m218/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5153 - loss: 1.1085
[1m249/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5144 - loss: 1.1108
[1m281/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5133 - loss: 1.1132
[1m309/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5124 - loss: 1.1150
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5118 - loss: 1.1160 - val_accuracy: 0.5239 - val_loss: 1.1376
Epoch 21/28

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 23ms/step - accuracy: 0.5625 - loss: 1.1528
[1m 30/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4917 - loss: 1.1403 
[1m 62/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4919 - loss: 1.1431
[1m 93/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4948 - loss: 1.1434
[1m124/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4968 - loss: 1.1429
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[1m184/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4985 - loss: 1.1441
[1m212/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4991 - loss: 1.1434
[1m246/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4998 - loss: 1.1423
[1m274/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5005 - loss: 1.1410
[1m304/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5015 - loss: 1.1399
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5021 - loss: 1.1392 - val_accuracy: 0.5348 - val_loss: 1.1364
Epoch 22/28

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[1m 31/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4872 - loss: 1.1665 
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Epoch 23/28

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[1m276/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5204 - loss: 1.1156
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Epoch 24/28

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[1m 32/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5158 - loss: 1.1143 
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[1m296/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5097 - loss: 1.1095
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[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5097 - loss: 1.1094 - val_accuracy: 0.5239 - val_loss: 1.1404
Epoch 25/28

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[1m 29/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5445 - loss: 1.0864 
[1m 59/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5383 - loss: 1.1000
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[1m119/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5285 - loss: 1.1094
[1m149/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5267 - loss: 1.1108
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[1m210/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5231 - loss: 1.1133
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[1m298/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5189 - loss: 1.1153
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Epoch 26/28

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 27ms/step - accuracy: 0.5625 - loss: 1.1517
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[1m 56/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5167 - loss: 1.1090
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Epoch 27/28

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.5000 - loss: 1.1937
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[1m278/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5166 - loss: 1.1121
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Epoch 28/28

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5000 - loss: 1.0552
[1m 27/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5370 - loss: 1.0565 
[1m 58/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5261 - loss: 1.0783
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[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5237 - loss: 1.0920 - val_accuracy: 0.5263 - val_loss: 1.1330

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m22s[0m 476ms/step2025-11-07 16:52:59.219014: 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_141', 8 bytes spill stores, 8 bytes spill loads


[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 22ms/step  
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 22ms/step
Saved model to disk.
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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Loaded model from disk.
(1545, 3, 250)
(10469, 3, 250)

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[1m176/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 866us/step
[1m231/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 877us/step
[1m293/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 864us/step
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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 16ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 18ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 18ms/step

[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m49/89[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step 
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step
Global accuracy score (validation) = 52.98 [%]
Global F1 score (validation) = 51.21 [%]
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[[0.488672   0.16341664 0.34020063 0.00771067]
 [0.06077649 0.02957062 0.84636545 0.06328748]
 [0.20440468 0.07552534 0.617829   0.10224101]
 ...
 [0.22539918 0.11284357 0.60226125 0.05949599]
 [0.22973026 0.14356783 0.5702248  0.05647714]
 [0.24255314 0.18302666 0.5103682  0.06405196]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 57.67 [%]
Global accuracy score (test) = 50.1 [%]
Global F1 score (train) = 56.92 [%]
Global F1 score (test) = 49.06 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.40      0.26      0.31       400
MODERATE-INTENSITY       0.49      0.58      0.53       400
         SEDENTARY       0.48      0.70      0.57       400
VIGOROUS-INTENSITY       0.66      0.46      0.54       345

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


Accuracy capturado en la ejecución 6: 50.1 [%]
F1-score capturado en la ejecución 6: 49.06 [%]

=== EJECUCIÓN 7 ===

--- TRAIN (ejecución 7) ---

--- TEST (ejecución 7) ---
2025-11-07 16:53:10.266089: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 16:53:10.277573: 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 00:00:1762530790.290625 3287309 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762530790.294828 3287309 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762530790.304890 3287309 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762530790.304910 3287309 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762530790.304912 3287309 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762530790.304913 3287309 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 16:53:10.307907: 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.
I0000 00:00:1762530792.548248 3287309 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13760 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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Epoch 1/28
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762530795.022000 3287441 service.cc:152] XLA service 0x71a310014e90 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762530795.022037 3287441 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-07 16:53:15.077827: 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 00:00:1762530795.404864 3287441 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762530797.712729 3287441 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m24:41[0m 5s/step - accuracy: 0.1562 - loss: 2.4137
[1m 27/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2407 - loss: 2.0747  
[1m 59/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2610 - loss: 2.0458
[1m 91/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2721 - loss: 2.0169
[1m120/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2785 - loss: 1.9940
[1m150/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2831 - loss: 1.9733
[1m180/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2872 - loss: 1.9569
[1m210/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2913 - loss: 1.9407
[1m242/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2954 - loss: 1.9255
[1m275/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2992 - loss: 1.9109
[1m303/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.3019 - loss: 1.9005
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step - accuracy: 0.3040 - loss: 1.8923
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m9s[0m 12ms/step - accuracy: 0.3040 - loss: 1.8920 - val_accuracy: 0.3996 - val_loss: 1.3949
Epoch 2/28

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.3750 - loss: 1.6391
[1m 32/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3425 - loss: 1.6393 
[1m 65/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3481 - loss: 1.6350
[1m 97/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3515 - loss: 1.6359
[1m127/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3532 - loss: 1.6367
[1m160/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3545 - loss: 1.6384
[1m192/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3563 - loss: 1.6380
[1m221/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3582 - loss: 1.6362
[1m251/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3600 - loss: 1.6339
[1m280/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3613 - loss: 1.6316
[1m312/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.3627 - loss: 1.6289
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Epoch 3/28

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[1m283/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3992 - loss: 1.5267
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Epoch 4/28

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[1m180/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3986 - loss: 1.4496
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[1m241/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3986 - loss: 1.4511
[1m269/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3988 - loss: 1.4516
[1m299/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.3991 - loss: 1.4517
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Epoch 5/28

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[1m 91/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3942 - loss: 1.4601
[1m121/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3967 - loss: 1.4543
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[1m184/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4014 - loss: 1.4456
[1m216/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4036 - loss: 1.4407
[1m247/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4050 - loss: 1.4374
[1m280/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4060 - loss: 1.4347
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[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4071 - loss: 1.4317 - val_accuracy: 0.4610 - val_loss: 1.2504
Epoch 6/28

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[1m 27/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4441 - loss: 1.2933 
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[1m124/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4335 - loss: 1.3484
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Epoch 7/28

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Epoch 8/28

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[1m282/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4431 - loss: 1.3280
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Epoch 9/28

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[1m179/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4341 - loss: 1.3308
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[1m273/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4390 - loss: 1.3222
[1m302/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4399 - loss: 1.3205
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4407 - loss: 1.3190 - val_accuracy: 0.4726 - val_loss: 1.2104
Epoch 10/28

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[1m124/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4449 - loss: 1.3042
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[1m185/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4458 - loss: 1.3069
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[1m245/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4464 - loss: 1.3073
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Epoch 11/28

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[1m272/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4567 - loss: 1.2608
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Epoch 12/28

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[1m180/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4458 - loss: 1.2909
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[1m271/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4481 - loss: 1.2855
[1m300/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4486 - loss: 1.2840
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Epoch 13/28

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[1m116/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4595 - loss: 1.2661
[1m149/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4610 - loss: 1.2607
[1m180/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4614 - loss: 1.2576
[1m211/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4617 - loss: 1.2555
[1m243/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4618 - loss: 1.2542
[1m275/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4623 - loss: 1.2527
[1m304/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4628 - loss: 1.2513
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4632 - loss: 1.2504 - val_accuracy: 0.4993 - val_loss: 1.1725
Epoch 14/28

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[1m 31/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4823 - loss: 1.2301 
[1m 62/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4850 - loss: 1.2303
[1m 94/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4832 - loss: 1.2354
[1m127/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4817 - loss: 1.2379
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[1m190/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4794 - loss: 1.2397
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[1m255/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4779 - loss: 1.2396
[1m286/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4776 - loss: 1.2391
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Epoch 15/28

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Epoch 16/28

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Epoch 17/28

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[1m297/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4780 - loss: 1.1998
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Epoch 18/28

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[1m273/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4798 - loss: 1.1758
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Epoch 19/28

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[1m 31/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5045 - loss: 1.1520 
[1m 58/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5032 - loss: 1.1529
[1m 90/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4993 - loss: 1.1548
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[1m272/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4896 - loss: 1.1661
[1m301/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4891 - loss: 1.1671
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Epoch 20/28

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[1m274/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4935 - loss: 1.1498
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Epoch 21/28

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[1m 28/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4785 - loss: 1.1888 
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[1m172/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4810 - loss: 1.1803
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[1m235/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4828 - loss: 1.1746
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[1m295/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4842 - loss: 1.1707
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Epoch 22/28

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[1m 30/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4763 - loss: 1.1957 
[1m 61/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4835 - loss: 1.1800
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[1m274/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4995 - loss: 1.1568
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Epoch 23/28

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[1m 28/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4893 - loss: 1.1667 
[1m 58/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4910 - loss: 1.1563
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[1m243/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4923 - loss: 1.1580
[1m274/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4921 - loss: 1.1580
[1m305/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4922 - loss: 1.1578
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Epoch 24/28

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[1m 30/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5156 - loss: 1.1181 
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[1m234/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5001 - loss: 1.1463
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[1m295/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5013 - loss: 1.1444
[1m327/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.5019 - loss: 1.1438
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5019 - loss: 1.1438 - val_accuracy: 0.5225 - val_loss: 1.1412
Epoch 25/28

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[1m121/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4934 - loss: 1.1499
[1m153/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4946 - loss: 1.1483
[1m186/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4959 - loss: 1.1461
[1m218/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4966 - loss: 1.1444
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[1m281/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4981 - loss: 1.1419
[1m310/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4988 - loss: 1.1412
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4992 - loss: 1.1408 - val_accuracy: 0.5207 - val_loss: 1.1343
Epoch 26/28

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[1m 31/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5621 - loss: 1.0957 
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[1m 89/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5324 - loss: 1.1190
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[1m291/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5194 - loss: 1.1177
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[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5191 - loss: 1.1173 - val_accuracy: 0.5197 - val_loss: 1.1398
Epoch 27/28

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.5000 - loss: 0.9710
[1m 30/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5297 - loss: 1.0534 
[1m 59/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5300 - loss: 1.0677
[1m 87/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5270 - loss: 1.0761
[1m119/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5245 - loss: 1.0823
[1m150/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5228 - loss: 1.0867
[1m179/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5212 - loss: 1.0904
[1m210/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5200 - loss: 1.0940
[1m239/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5193 - loss: 1.0966
[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5188 - loss: 1.0988
[1m300/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5183 - loss: 1.1006
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5180 - loss: 1.1020
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5180 - loss: 1.1020 - val_accuracy: 0.5091 - val_loss: 1.1519
Epoch 28/28

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4375 - loss: 1.2866
[1m 28/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5118 - loss: 1.1353 
[1m 61/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5211 - loss: 1.1083
[1m 93/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5212 - loss: 1.1077
[1m121/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5203 - loss: 1.1094
[1m151/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5191 - loss: 1.1117
[1m181/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5180 - loss: 1.1132
[1m214/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5174 - loss: 1.1138
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[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5177 - loss: 1.1128 - val_accuracy: 0.5179 - val_loss: 1.1400

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m22s[0m 479ms/step2025-11-07 16:53:42.468506: 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_141', 8 bytes spill stores, 8 bytes spill loads


[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 21ms/step  
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 22ms/step
Saved model to disk.
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Loaded model from disk.
(1545, 3, 250)
(10469, 3, 250)

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7:22[0m 1s/step
[1m 51/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step 
[1m107/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 954us/step
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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 15ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 18ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 19ms/step

[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 17ms/step
[1m52/89[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 993us/step
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step  
Global accuracy score (validation) = 52.81 [%]
Global F1 score (validation) = 50.01 [%]
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[[0.38310412 0.21697666 0.3880468  0.0118724 ]
 [0.3855304  0.21962956 0.38303798 0.0118021 ]
 [0.03144734 0.03456215 0.91678166 0.01720887]
 ...
 [0.29967308 0.10618132 0.481673   0.11247259]
 [0.34365335 0.08924169 0.46512517 0.1019798 ]
 [0.20185849 0.14828888 0.5889494  0.06090327]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 56.89 [%]
Global accuracy score (test) = 48.61 [%]
Global F1 score (train) = 55.64 [%]
Global F1 score (test) = 46.17 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.37      0.15      0.22       400
MODERATE-INTENSITY       0.46      0.61      0.52       400
         SEDENTARY       0.48      0.72      0.58       400
VIGOROUS-INTENSITY       0.62      0.46      0.53       345

          accuracy                           0.49      1545
         macro avg       0.48      0.49      0.46      1545
      weighted avg       0.48      0.49      0.46      1545


Accuracy capturado en la ejecución 7: 48.61 [%]
F1-score capturado en la ejecución 7: 46.17 [%]

=== EJECUCIÓN 8 ===

--- TRAIN (ejecución 8) ---

--- TEST (ejecución 8) ---
2025-11-07 16:53:53.488886: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 16:53:53.500213: 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 00:00:1762530833.513698 3290988 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762530833.518053 3290988 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762530833.528266 3290988 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762530833.528287 3290988 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762530833.528289 3290988 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762530833.528291 3290988 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 16:53:53.531498: 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.
I0000 00:00:1762530835.796383 3290988 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13760 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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Epoch 1/28
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762530838.282266 3291099 service.cc:152] XLA service 0x7de398007de0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762530838.282328 3291099 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-07 16:53:58.337263: 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 00:00:1762530838.662998 3291099 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762530841.037438 3291099 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m25:01[0m 5s/step - accuracy: 0.3750 - loss: 1.6474
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[1m209/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3249 - loss: 1.7336
[1m239/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3272 - loss: 1.7273
[1m271/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3293 - loss: 1.7213
[1m302/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.3311 - loss: 1.7156
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Epoch 2/28

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[1m 32/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3754 - loss: 1.5577 
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[1m 94/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3872 - loss: 1.5405
[1m124/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3874 - loss: 1.5410
[1m155/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3878 - loss: 1.5400
[1m183/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3880 - loss: 1.5382
[1m214/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3879 - loss: 1.5370
[1m246/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3876 - loss: 1.5358
[1m279/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3878 - loss: 1.5339
[1m310/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.3880 - loss: 1.5319
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Epoch 3/28

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[1m 32/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4114 - loss: 1.4172 
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Epoch 4/28

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[1m 29/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4129 - loss: 1.4697 
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[1m245/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4187 - loss: 1.4100
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[1m308/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4188 - loss: 1.4080
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Epoch 5/28

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Epoch 6/28

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[1m 33/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4435 - loss: 1.3176 
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[1m184/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4342 - loss: 1.3292
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Epoch 7/28

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[1m 30/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4436 - loss: 1.2632 
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[1m 91/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4436 - loss: 1.2894
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Epoch 8/28

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Epoch 9/28

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Epoch 10/28

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[1m298/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4545 - loss: 1.2753
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Epoch 11/28

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[1m 31/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4631 - loss: 1.2661 
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[1m 92/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4629 - loss: 1.2522
[1m124/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4628 - loss: 1.2506
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Epoch 12/28

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[1m 30/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4450 - loss: 1.3053 
[1m 60/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4583 - loss: 1.2736
[1m 92/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4633 - loss: 1.2567
[1m124/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4663 - loss: 1.2464
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[1m244/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4694 - loss: 1.2337
[1m274/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4698 - loss: 1.2322
[1m304/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4701 - loss: 1.2311
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Epoch 13/28

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[1m 32/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4684 - loss: 1.2256 
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[1m 90/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4644 - loss: 1.2214
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[1m185/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4631 - loss: 1.2241
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[1m245/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4645 - loss: 1.2227
[1m278/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4651 - loss: 1.2219
[1m309/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4657 - loss: 1.2210
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Epoch 14/28

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[1m 31/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4622 - loss: 1.2605 
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[1m 93/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4672 - loss: 1.2333
[1m125/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4692 - loss: 1.2257
[1m158/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4698 - loss: 1.2215
[1m189/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4700 - loss: 1.2189
[1m222/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4702 - loss: 1.2165
[1m252/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4706 - loss: 1.2145
[1m281/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4711 - loss: 1.2128
[1m312/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.4717 - loss: 1.2112
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4720 - loss: 1.2103 - val_accuracy: 0.5200 - val_loss: 1.1236
Epoch 15/28

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[1m 31/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4742 - loss: 1.2080 
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[1m 91/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4790 - loss: 1.2010
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[1m182/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4857 - loss: 1.1887
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[1m243/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4862 - loss: 1.1873
[1m273/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4864 - loss: 1.1869
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Epoch 16/28

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Epoch 17/28

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Epoch 18/28

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Epoch 19/28

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[1m 89/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4983 - loss: 1.1652
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[1m180/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4953 - loss: 1.1640
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Epoch 20/28

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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m22s[0m 476ms/step2025-11-07 16:54:20.034818: 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_141', 8 bytes spill stores, 8 bytes spill loads

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Saved model to disk.
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Loaded model from disk.
(1545, 3, 250)
(10469, 3, 250)

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[1m55/89[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 930us/step
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Global accuracy score (validation) = 52.0 [%]
Global F1 score (validation) = 49.85 [%]
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[[0.04040135 0.02959025 0.90238804 0.02762045]
 [0.13304676 0.09003451 0.6231838  0.15373488]
 [0.32723212 0.34129655 0.04927704 0.28219417]
 ...
 [0.28116328 0.21862936 0.42214665 0.07806069]
 [0.28440246 0.14323719 0.4378708  0.1344895 ]
 [0.269795   0.16143051 0.47321114 0.09556348]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 56.18 [%]
Global accuracy score (test) = 50.42 [%]
Global F1 score (train) = 55.32 [%]
Global F1 score (test) = 49.05 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.46      0.27      0.34       400
MODERATE-INTENSITY       0.48      0.49      0.48       400
         SEDENTARY       0.48      0.79      0.60       400
VIGOROUS-INTENSITY       0.66      0.46      0.54       345

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


Accuracy capturado en la ejecución 8: 50.42 [%]
F1-score capturado en la ejecución 8: 49.05 [%]

=== EJECUCIÓN 9 ===

--- TRAIN (ejecución 9) ---

--- TEST (ejecución 9) ---
2025-11-07 16:54:31.033117: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 16:54:31.044570: 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 00:00:1762530871.057652 3293889 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762530871.061992 3293889 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762530871.072031 3293889 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762530871.072051 3293889 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762530871.072053 3293889 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762530871.072055 3293889 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 16:54:31.075200: 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.
I0000 00:00:1762530873.320086 3293889 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13760 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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Epoch 1/28
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762530875.754150 3294023 service.cc:152] XLA service 0x78451c003450 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762530875.754179 3294023 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-07 16:54:35.804263: 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 00:00:1762530876.116972 3294023 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762530878.511387 3294023 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 2/28

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Epoch 3/28

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Epoch 4/28

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Epoch 5/28

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[1m 28/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4319 - loss: 1.3238 
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[1m274/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4273 - loss: 1.3512
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Epoch 6/28

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Epoch 7/28

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[1m124/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4217 - loss: 1.3478
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[1m212/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4292 - loss: 1.3358
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Epoch 8/28

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[1m 28/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4580 - loss: 1.2663 
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Epoch 9/28

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Epoch 10/28

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[1m296/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4633 - loss: 1.2475
[1m327/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.4631 - loss: 1.2480
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Epoch 11/28

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[1m271/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4618 - loss: 1.2440
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Epoch 12/28

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 23ms/step - accuracy: 0.6562 - loss: 0.9022
[1m 28/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5096 - loss: 1.1524 
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Epoch 13/28

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[1m 29/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4443 - loss: 1.2281 
[1m 59/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4580 - loss: 1.2237
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[1m266/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4648 - loss: 1.2236
[1m296/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4650 - loss: 1.2237
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Epoch 14/28

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[1m 32/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4672 - loss: 1.2316 
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[1m279/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4637 - loss: 1.2275
[1m310/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4648 - loss: 1.2258
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Epoch 15/28

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[1m 26/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4736 - loss: 1.2571 
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[1m179/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4622 - loss: 1.2399
[1m209/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4631 - loss: 1.2363
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[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4650 - loss: 1.2305
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Epoch 16/28

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[1m 31/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4995 - loss: 1.1328 
[1m 60/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4964 - loss: 1.1460
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[1m271/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4842 - loss: 1.1764
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Epoch 17/28

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[1m 27/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5009 - loss: 1.1692 
[1m 58/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4914 - loss: 1.1864
[1m 88/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4858 - loss: 1.1911
[1m118/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4820 - loss: 1.1939
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[1m210/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4805 - loss: 1.1935
[1m241/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4810 - loss: 1.1925
[1m272/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4814 - loss: 1.1919
[1m299/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4816 - loss: 1.1913
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Epoch 18/28

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[1m 28/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4751 - loss: 1.1935 
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[1m 87/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4817 - loss: 1.1791
[1m116/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4842 - loss: 1.1757
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[1m173/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4881 - loss: 1.1715
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[1m230/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4895 - loss: 1.1700
[1m260/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4903 - loss: 1.1689
[1m288/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4908 - loss: 1.1681
[1m319/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.4911 - loss: 1.1677
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4911 - loss: 1.1676 - val_accuracy: 0.4982 - val_loss: 1.2025
Epoch 19/28

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[1m 30/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4843 - loss: 1.1700 
[1m 58/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4855 - loss: 1.1680
[1m 89/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4886 - loss: 1.1653
[1m118/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4908 - loss: 1.1644
[1m149/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4925 - loss: 1.1626
[1m178/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4940 - loss: 1.1609
[1m208/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4948 - loss: 1.1599
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[1m268/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4956 - loss: 1.1592
[1m297/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4961 - loss: 1.1590
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4966 - loss: 1.1587 - val_accuracy: 0.5137 - val_loss: 1.1814
Epoch 20/28

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[1m 29/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5522 - loss: 1.1083 
[1m 56/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5395 - loss: 1.1204
[1m 85/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5327 - loss: 1.1229
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Epoch 21/28

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[1m 31/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4799 - loss: 1.1789 
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[1m 91/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4933 - loss: 1.1603
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[1m182/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4959 - loss: 1.1545
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[1m245/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4968 - loss: 1.1526
[1m275/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4974 - loss: 1.1518
[1m303/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4978 - loss: 1.1514
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Epoch 22/28

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[1m 25/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5239 - loss: 1.1090 
[1m 58/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5181 - loss: 1.1206
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[1m179/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5122 - loss: 1.1254
[1m208/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5116 - loss: 1.1257
[1m239/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5108 - loss: 1.1267
[1m269/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5099 - loss: 1.1278
[1m298/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5089 - loss: 1.1289
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5080 - loss: 1.1299 - val_accuracy: 0.5088 - val_loss: 1.1723
Epoch 23/28

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.4375 - loss: 1.1892
[1m 29/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5045 - loss: 1.1453 
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[1m 93/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5133 - loss: 1.1246
[1m122/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5139 - loss: 1.1242
[1m153/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5125 - loss: 1.1246
[1m181/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5115 - loss: 1.1251
[1m208/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5105 - loss: 1.1256
[1m240/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5099 - loss: 1.1258
[1m272/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5095 - loss: 1.1257
[1m303/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5095 - loss: 1.1254
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5094 - loss: 1.1251 - val_accuracy: 0.5144 - val_loss: 1.1697
Epoch 24/28

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.4375 - loss: 1.1536
[1m 31/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5359 - loss: 1.1041 
[1m 62/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5227 - loss: 1.1107
[1m 92/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5183 - loss: 1.1141
[1m123/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5143 - loss: 1.1175
[1m153/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5124 - loss: 1.1192
[1m182/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5111 - loss: 1.1210
[1m212/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5102 - loss: 1.1227
[1m242/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5089 - loss: 1.1249
[1m273/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5082 - loss: 1.1263
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Epoch 25/28

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[1m 31/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4989 - loss: 1.1391 
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Epoch 26/28

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[1m274/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5136 - loss: 1.1002
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Epoch 27/28

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[1m175/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5213 - loss: 1.0967
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[1m236/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5235 - loss: 1.0950
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[1m293/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5246 - loss: 1.0943
[1m323/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.5247 - loss: 1.0945
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5247 - loss: 1.0945 - val_accuracy: 0.5211 - val_loss: 1.1503
Epoch 28/28

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[1m 29/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4899 - loss: 1.1078 
[1m 58/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4924 - loss: 1.1211
[1m 85/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4958 - loss: 1.1238
[1m114/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4994 - loss: 1.1234
[1m144/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5017 - loss: 1.1218
[1m175/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5039 - loss: 1.1199
[1m207/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5055 - loss: 1.1188
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[1m299/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5078 - loss: 1.1168
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5083 - loss: 1.1164
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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m24s[0m 506ms/step2025-11-07 16:55:03.516649: 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_141', 8 bytes spill stores, 8 bytes spill loads

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Saved model to disk.
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Loaded model from disk.
(1545, 3, 250)
(10469, 3, 250)

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7:03[0m 1s/step
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[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m62/89[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 826us/step
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step  
Global accuracy score (validation) = 50.49 [%]
Global F1 score (validation) = 48.33 [%]
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[[0.41874817 0.24945508 0.3090499  0.02274689]
 [0.05005834 0.04948654 0.8686816  0.03177356]
 [0.2251688  0.08990848 0.5431366  0.14178619]
 ...
 [0.23139516 0.11353666 0.56273025 0.09233794]
 [0.22012109 0.12528132 0.5389815  0.11561608]
 [0.2518474  0.135487   0.53642994 0.07623571]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 56.07 [%]
Global accuracy score (test) = 51.91 [%]
Global F1 score (train) = 54.77 [%]
Global F1 score (test) = 49.12 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.44      0.17      0.24       400
MODERATE-INTENSITY       0.48      0.73      0.58       400
         SEDENTARY       0.52      0.72      0.61       400
VIGOROUS-INTENSITY       0.67      0.45      0.54       345

          accuracy                           0.52      1545
         macro avg       0.53      0.52      0.49      1545
      weighted avg       0.52      0.52      0.49      1545


Accuracy capturado en la ejecución 9: 51.91 [%]
F1-score capturado en la ejecución 9: 49.12 [%]

=== EJECUCIÓN 10 ===

--- TRAIN (ejecución 10) ---

--- TEST (ejecución 10) ---
2025-11-07 16:55:14.486625: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 16:55:14.498106: 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 00:00:1762530914.511297 3297571 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762530914.515462 3297571 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762530914.525281 3297571 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762530914.525316 3297571 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762530914.525318 3297571 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762530914.525320 3297571 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 16:55:14.528676: 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.
I0000 00:00:1762530916.776315 3297571 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13760 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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Epoch 1/28
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762530919.274815 3297680 service.cc:152] XLA service 0x7c9008025bc0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762530919.274870 3297680 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-07 16:55:19.327846: 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 00:00:1762530919.657034 3297680 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762530921.984436 3297680 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m24:56[0m 5s/step - accuracy: 0.2500 - loss: 1.7513
[1m 27/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2373 - loss: 1.9813  
[1m 57/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2444 - loss: 1.9815
[1m 83/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2544 - loss: 1.9605
[1m115/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2640 - loss: 1.9356
[1m146/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2703 - loss: 1.9163
[1m177/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2760 - loss: 1.8989
[1m209/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2809 - loss: 1.8828
[1m239/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2851 - loss: 1.8688
[1m271/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2893 - loss: 1.8553
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Epoch 2/28

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[1m273/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3792 - loss: 1.5499
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Epoch 3/28

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Epoch 4/28

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[1m125/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4083 - loss: 1.4339
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[1m275/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4117 - loss: 1.4227
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Epoch 5/28

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[1m 30/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4388 - loss: 1.3750 
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Epoch 6/28

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[1m 27/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4718 - loss: 1.2578 
[1m 60/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4600 - loss: 1.2928
[1m 92/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4518 - loss: 1.3100
[1m124/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4469 - loss: 1.3215
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[1m243/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4417 - loss: 1.3300
[1m274/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4418 - loss: 1.3296
[1m304/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4417 - loss: 1.3297
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Epoch 7/28

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[1m 27/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4734 - loss: 1.2945 
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[1m 88/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4623 - loss: 1.2893
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[1m181/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4551 - loss: 1.2967
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[1m241/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4525 - loss: 1.3003
[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4517 - loss: 1.3014
[1m302/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4510 - loss: 1.3028
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Epoch 8/28

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[1m 30/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4818 - loss: 1.2555 
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[1m184/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4534 - loss: 1.3054
[1m213/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4531 - loss: 1.3052
[1m244/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4526 - loss: 1.3055
[1m275/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4520 - loss: 1.3059
[1m301/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4518 - loss: 1.3055
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4517 - loss: 1.3048 - val_accuracy: 0.5137 - val_loss: 1.1438
Epoch 9/28

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[1m 29/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4578 - loss: 1.2732 
[1m 59/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4631 - loss: 1.2675
[1m 90/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4639 - loss: 1.2662
[1m118/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4630 - loss: 1.2663
[1m148/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4621 - loss: 1.2675
[1m179/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4607 - loss: 1.2687
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[1m240/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4602 - loss: 1.2675
[1m271/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4604 - loss: 1.2665
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Epoch 10/28

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Epoch 11/28

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[1m242/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4708 - loss: 1.2379
[1m271/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4704 - loss: 1.2370
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Epoch 12/28

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[1m182/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4726 - loss: 1.2259
[1m209/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4731 - loss: 1.2248
[1m242/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4739 - loss: 1.2228
[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4744 - loss: 1.2215
[1m301/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4746 - loss: 1.2205
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4746 - loss: 1.2201 - val_accuracy: 0.5056 - val_loss: 1.1179
Epoch 13/28

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[1m 31/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5046 - loss: 1.1915 
[1m 58/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4942 - loss: 1.1904
[1m 90/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4915 - loss: 1.1897
[1m122/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4904 - loss: 1.1876
[1m154/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4891 - loss: 1.1877
[1m183/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4876 - loss: 1.1890
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[1m242/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4857 - loss: 1.1915
[1m271/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4851 - loss: 1.1921
[1m302/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4844 - loss: 1.1932
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Epoch 14/28

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[1m 29/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4989 - loss: 1.1931 
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Epoch 15/28

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Epoch 16/28

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[1m239/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4910 - loss: 1.1804
[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4902 - loss: 1.1802
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[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4888 - loss: 1.1802 - val_accuracy: 0.5207 - val_loss: 1.1059
Epoch 17/28

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[1m 27/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4611 - loss: 1.2067 
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Epoch 18/28

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[1m 27/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5088 - loss: 1.1113 
[1m 58/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5088 - loss: 1.1226
[1m 90/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5076 - loss: 1.1266
[1m119/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5071 - loss: 1.1283
[1m150/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5060 - loss: 1.1304
[1m180/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5052 - loss: 1.1325
[1m210/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5045 - loss: 1.1345
[1m241/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5038 - loss: 1.1361
[1m271/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5031 - loss: 1.1378
[1m302/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5027 - loss: 1.1391
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5025 - loss: 1.1398 - val_accuracy: 0.5232 - val_loss: 1.1122
Epoch 19/28

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[1m 32/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4848 - loss: 1.1548 
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[1m 95/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4933 - loss: 1.1490
[1m127/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4954 - loss: 1.1483
[1m158/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4977 - loss: 1.1471
[1m190/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4999 - loss: 1.1460
[1m222/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5015 - loss: 1.1457
[1m252/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5025 - loss: 1.1454
[1m283/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5033 - loss: 1.1450
[1m315/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.5036 - loss: 1.1451
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5036 - loss: 1.1452 - val_accuracy: 0.5232 - val_loss: 1.1063
Epoch 20/28

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[1m 32/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4862 - loss: 1.1092 
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[1m 94/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4833 - loss: 1.1377
[1m125/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4841 - loss: 1.1392
[1m157/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4851 - loss: 1.1395
[1m184/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4858 - loss: 1.1404
[1m214/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4868 - loss: 1.1406
[1m245/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4872 - loss: 1.1409
[1m278/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4878 - loss: 1.1411
[1m310/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4884 - loss: 1.1411
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4887 - loss: 1.1411 - val_accuracy: 0.5200 - val_loss: 1.1048
Epoch 21/28

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.5625 - loss: 1.0469
[1m 29/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5566 - loss: 1.0096 
[1m 60/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5432 - loss: 1.0403
[1m 91/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5356 - loss: 1.0558
[1m118/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5299 - loss: 1.0664
[1m149/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5255 - loss: 1.0762
[1m181/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5220 - loss: 1.0841
[1m210/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5201 - loss: 1.0890
[1m241/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5184 - loss: 1.0932
[1m269/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5170 - loss: 1.0971
[1m299/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5155 - loss: 1.1011
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5142 - loss: 1.1043 - val_accuracy: 0.5179 - val_loss: 1.1036
Epoch 22/28

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[1m 31/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4834 - loss: 1.1537 
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[1m309/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5037 - loss: 1.1273
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Epoch 23/28

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.5000 - loss: 1.1599
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[1m282/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5119 - loss: 1.1127
[1m311/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5123 - loss: 1.1126
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Epoch 24/28

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[1m121/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5040 - loss: 1.1210
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[1m187/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5081 - loss: 1.1176
[1m216/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5088 - loss: 1.1171
[1m243/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5095 - loss: 1.1166
[1m271/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5102 - loss: 1.1160
[1m300/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5107 - loss: 1.1155
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5112 - loss: 1.1151 - val_accuracy: 0.5130 - val_loss: 1.1012
Epoch 25/28

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[1m 28/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5004 - loss: 1.1309 
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[1m 89/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5117 - loss: 1.1039
[1m121/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5144 - loss: 1.0995
[1m155/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5142 - loss: 1.1001
[1m187/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5143 - loss: 1.1003
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[1m248/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5144 - loss: 1.1004
[1m279/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5144 - loss: 1.1008
[1m309/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5144 - loss: 1.1010
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Epoch 26/28

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Epoch 27/28

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Epoch 28/28

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[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5245 - loss: 1.0890 - val_accuracy: 0.5140 - val_loss: 1.1032

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m23s[0m 484ms/step2025-11-07 16:55:46.762675: 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_141', 8 bytes spill stores, 8 bytes spill loads


[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 22ms/step  
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 22ms/step
Saved model to disk.
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
Loaded model from disk.
(1545, 3, 250)
(10469, 3, 250)

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[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step  
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m3s[0m 4ms/step

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 15ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 18ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 19ms/step

[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m57/89[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 905us/step
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step  
Global accuracy score (validation) = 51.97 [%]
Global F1 score (validation) = 48.34 [%]
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[[0.17171642 0.11470248 0.6287573  0.08482388]
 [0.06578425 0.37246704 0.5157819  0.04596689]
 [0.15110832 0.08529881 0.6525201  0.11107283]
 ...
 [0.19724959 0.12881488 0.63396376 0.03997179]
 [0.17328884 0.12371818 0.5946951  0.10829794]
 [0.1606697  0.15968503 0.644858   0.0347873 ]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 55.28 [%]
Global accuracy score (test) = 48.74 [%]
Global F1 score (train) = 53.85 [%]
Global F1 score (test) = 46.48 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.40      0.17      0.24       400
MODERATE-INTENSITY       0.48      0.54      0.51       400
         SEDENTARY       0.45      0.78      0.57       400
VIGOROUS-INTENSITY       0.66      0.45      0.54       345

          accuracy                           0.49      1545
         macro avg       0.50      0.49      0.46      1545
      weighted avg       0.49      0.49      0.46      1545


Accuracy capturado en la ejecución 10: 48.74 [%]
F1-score capturado en la ejecución 10: 46.48 [%]

=== EJECUCIÓN 11 ===

--- TRAIN (ejecución 11) ---

--- TEST (ejecución 11) ---
2025-11-07 16:55:57.764575: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 16:55:57.775968: 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 00:00:1762530957.789091 3301223 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762530957.793210 3301223 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762530957.802950 3301223 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762530957.802967 3301223 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762530957.802970 3301223 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762530957.802972 3301223 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 16:55:57.806174: 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.
I0000 00:00:1762530960.032777 3301223 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13760 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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Epoch 1/28
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762530962.496597 3301353 service.cc:152] XLA service 0x71c5fc015200 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762530962.496629 3301353 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-07 16:56:02.551602: 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 00:00:1762530962.881151 3301353 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762530965.188222 3301353 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m24:36[0m 5s/step - accuracy: 0.3125 - loss: 1.7759
[1m 29/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2865 - loss: 1.7797  
[1m 57/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2942 - loss: 1.7471
[1m 86/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2974 - loss: 1.7300
[1m116/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3010 - loss: 1.7145
[1m146/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3035 - loss: 1.7051
[1m177/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3061 - loss: 1.6973
[1m210/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3092 - loss: 1.6885
[1m241/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3120 - loss: 1.6803
[1m272/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3146 - loss: 1.6721
[1m301/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.3169 - loss: 1.6652
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step - accuracy: 0.3189 - loss: 1.6590
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m9s[0m 13ms/step - accuracy: 0.3190 - loss: 1.6587 - val_accuracy: 0.4242 - val_loss: 1.2332
Epoch 2/28

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[1m 30/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3789 - loss: 1.5194 
[1m 60/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3792 - loss: 1.5143
[1m 91/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3803 - loss: 1.5081
[1m122/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3829 - loss: 1.5008
[1m152/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3842 - loss: 1.4955
[1m183/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3844 - loss: 1.4928
[1m216/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3847 - loss: 1.4901
[1m246/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3847 - loss: 1.4881
[1m277/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3849 - loss: 1.4858
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Epoch 3/28

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Epoch 4/28

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[1m278/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4237 - loss: 1.3628
[1m310/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4240 - loss: 1.3615
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Epoch 5/28

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[1m181/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4115 - loss: 1.3598
[1m210/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4134 - loss: 1.3555
[1m241/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4149 - loss: 1.3517
[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4163 - loss: 1.3482
[1m300/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4177 - loss: 1.3452
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4190 - loss: 1.3428 - val_accuracy: 0.4930 - val_loss: 1.1880
Epoch 6/28

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[1m 31/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4612 - loss: 1.2399 
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[1m 93/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4561 - loss: 1.2615
[1m123/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4539 - loss: 1.2681
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[1m182/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4516 - loss: 1.2756
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[1m246/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4513 - loss: 1.2782
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Epoch 7/28

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[1m279/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4451 - loss: 1.2827
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Epoch 8/28

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[1m277/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4485 - loss: 1.2655
[1m305/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4488 - loss: 1.2648
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Epoch 9/28

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[1m119/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4743 - loss: 1.2310
[1m151/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4713 - loss: 1.2352
[1m180/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4692 - loss: 1.2372
[1m210/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4674 - loss: 1.2389
[1m240/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4664 - loss: 1.2399
[1m272/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4658 - loss: 1.2403
[1m303/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4653 - loss: 1.2404
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4652 - loss: 1.2403 - val_accuracy: 0.5042 - val_loss: 1.1654
Epoch 10/28

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[1m 31/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4797 - loss: 1.1997 
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[1m 91/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4644 - loss: 1.2191
[1m122/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4613 - loss: 1.2251
[1m153/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4595 - loss: 1.2286
[1m184/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4590 - loss: 1.2301
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[1m245/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4597 - loss: 1.2315
[1m274/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4601 - loss: 1.2319
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Epoch 11/28

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[1m 29/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5044 - loss: 1.1858 
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[1m187/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4931 - loss: 1.2000
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Epoch 12/28

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[1m310/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4661 - loss: 1.2228
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Epoch 13/28

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[1m120/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4844 - loss: 1.2021
[1m150/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4831 - loss: 1.2015
[1m179/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4821 - loss: 1.2011
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[1m241/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4810 - loss: 1.1995
[1m271/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4803 - loss: 1.1994
[1m298/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4798 - loss: 1.1993
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4794 - loss: 1.1992
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4794 - loss: 1.1992 - val_accuracy: 0.5074 - val_loss: 1.1438
Epoch 14/28

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5625 - loss: 1.1153
[1m 28/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4994 - loss: 1.1549 
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[1m 91/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4908 - loss: 1.1675
[1m122/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4909 - loss: 1.1689
[1m151/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4905 - loss: 1.1707
[1m181/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4898 - loss: 1.1725
[1m209/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4899 - loss: 1.1727
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[1m274/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4903 - loss: 1.1729
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Epoch 15/28

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[1m 30/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5126 - loss: 1.1191 
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Epoch 16/28

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Epoch 17/28

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Epoch 18/28

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Epoch 19/28

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[1m 29/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4786 - loss: 1.1687 
[1m 60/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4867 - loss: 1.1479
[1m 92/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4898 - loss: 1.1432
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[1m272/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4963 - loss: 1.1353
[1m304/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4967 - loss: 1.1352
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Epoch 20/28

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[1m 29/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4994 - loss: 1.1900 
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[1m291/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5058 - loss: 1.1440
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Epoch 21/28

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[1m 29/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5133 - loss: 1.1355 
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[1m115/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5194 - loss: 1.1308
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[1m200/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5188 - loss: 1.1299
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[1m291/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5181 - loss: 1.1286
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[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5178 - loss: 1.1282 - val_accuracy: 0.5190 - val_loss: 1.1277
Epoch 22/28

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[1m 28/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5372 - loss: 1.0645 
[1m 57/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5262 - loss: 1.0835
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Epoch 23/28

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[1m296/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5208 - loss: 1.1064
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Epoch 24/28

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[1m128/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5283 - loss: 1.1191
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[1m192/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5266 - loss: 1.1122
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[1m287/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5250 - loss: 1.1096
[1m319/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.5246 - loss: 1.1093
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5246 - loss: 1.1092 - val_accuracy: 0.5109 - val_loss: 1.1313
Epoch 25/28

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[1m 28/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5023 - loss: 1.1698 
[1m 58/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5139 - loss: 1.1445
[1m 86/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5169 - loss: 1.1330
[1m114/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5182 - loss: 1.1275
[1m141/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5193 - loss: 1.1228
[1m172/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5197 - loss: 1.1191
[1m202/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5199 - loss: 1.1175
[1m233/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5203 - loss: 1.1162
[1m261/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5205 - loss: 1.1155
[1m292/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5210 - loss: 1.1148
[1m324/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.5212 - loss: 1.1143
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5213 - loss: 1.1142 - val_accuracy: 0.5133 - val_loss: 1.1277
Epoch 26/28

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.5000 - loss: 1.0386
[1m 29/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5451 - loss: 1.0909 
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[1m 89/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5331 - loss: 1.0978
[1m119/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5319 - loss: 1.0981
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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m23s[0m 484ms/step2025-11-07 16:56:28.707850: 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_141', 8 bytes spill stores, 8 bytes spill loads

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Saved model to disk.
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Loaded model from disk.
(1545, 3, 250)
(10469, 3, 250)

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7:04[0m 1s/step
[1m 57/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 904us/step
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[1m167/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 912us/step
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[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 18ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 18ms/step

[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 15ms/step
[1m61/89[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 844us/step
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 977us/step
Global accuracy score (validation) = 52.49 [%]
Global F1 score (validation) = 51.97 [%]
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[[0.47928905 0.2163453  0.2866964  0.01766927]
 [0.3200925  0.14140473 0.45172542 0.08677735]
 [0.15686189 0.07498205 0.6402578  0.12789835]
 ...
 [0.3338722  0.14976834 0.46397573 0.05238378]
 [0.3726192  0.13835289 0.43644503 0.0525829 ]
 [0.30763856 0.17991436 0.47062382 0.04182328]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 57.8 [%]
Global accuracy score (test) = 49.71 [%]
Global F1 score (train) = 57.75 [%]
Global F1 score (test) = 49.53 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.40      0.39      0.39       400
MODERATE-INTENSITY       0.49      0.44      0.46       400
         SEDENTARY       0.50      0.70      0.58       400
VIGOROUS-INTENSITY       0.67      0.46      0.54       345

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


Accuracy capturado en la ejecución 11: 49.71 [%]
F1-score capturado en la ejecución 11: 49.53 [%]

=== EJECUCIÓN 12 ===

--- TRAIN (ejecución 12) ---

--- TEST (ejecución 12) ---
2025-11-07 16:56:39.738882: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 16:56:39.750377: 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 00:00:1762530999.763506 3304685 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762530999.767570 3304685 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762530999.777689 3304685 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762530999.777709 3304685 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762530999.777712 3304685 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762530999.777714 3304685 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 16:56:39.780732: 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.
I0000 00:00:1762531002.036653 3304685 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13758 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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Epoch 1/28
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762531004.493299 3304815 service.cc:152] XLA service 0x7a8330015050 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762531004.493351 3304815 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-07 16:56:44.544310: 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 00:00:1762531004.857857 3304815 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762531007.181743 3304815 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m24:35[0m 5s/step - accuracy: 0.2812 - loss: 2.0151
[1m 25/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2540 - loss: 2.0955  
[1m 53/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2559 - loss: 2.0564
[1m 85/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2638 - loss: 2.0205
[1m118/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2709 - loss: 1.9925
[1m149/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2769 - loss: 1.9695
[1m180/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2826 - loss: 1.9499
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Epoch 2/28

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[1m281/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3695 - loss: 1.6155
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Epoch 3/28

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Epoch 4/28

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[1m 32/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3821 - loss: 1.5627 
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[1m 95/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3932 - loss: 1.5210
[1m127/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3960 - loss: 1.5120
[1m158/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3981 - loss: 1.5038
[1m188/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3994 - loss: 1.4988
[1m220/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4006 - loss: 1.4946
[1m253/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4023 - loss: 1.4898
[1m284/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4035 - loss: 1.4858
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Epoch 5/28

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[1m 31/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4224 - loss: 1.4231 
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Epoch 6/28

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Epoch 7/28

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Epoch 8/28

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[1m 29/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4283 - loss: 1.3571 
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[1m 86/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4430 - loss: 1.3304
[1m118/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4474 - loss: 1.3267
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[1m266/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4535 - loss: 1.3223
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Epoch 9/28

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[1m 32/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4595 - loss: 1.3175 
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Epoch 10/28

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[1m275/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4612 - loss: 1.2773
[1m305/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4609 - loss: 1.2765
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Epoch 11/28

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[1m126/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4559 - loss: 1.2497
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[1m183/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4574 - loss: 1.2501
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[1m280/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4590 - loss: 1.2510
[1m312/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.4597 - loss: 1.2509
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4599 - loss: 1.2511 - val_accuracy: 0.5341 - val_loss: 1.1645
Epoch 12/28

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[1m 28/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4793 - loss: 1.2249 
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[1m 92/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4668 - loss: 1.2438
[1m122/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4650 - loss: 1.2462
[1m154/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4653 - loss: 1.2465
[1m185/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4657 - loss: 1.2469
[1m218/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4653 - loss: 1.2475
[1m250/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4650 - loss: 1.2479
[1m278/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4647 - loss: 1.2484
[1m310/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4648 - loss: 1.2485
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4648 - loss: 1.2485 - val_accuracy: 0.5435 - val_loss: 1.1569
Epoch 13/28

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.2812 - loss: 1.5229
[1m 30/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4586 - loss: 1.2151 
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[1m277/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4642 - loss: 1.2350
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Epoch 14/28

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[1m298/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4731 - loss: 1.2163
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Epoch 15/28

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[1m278/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4795 - loss: 1.1949
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[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4793 - loss: 1.1946 - val_accuracy: 0.5509 - val_loss: 1.1413
Epoch 16/28

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[1m 31/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4652 - loss: 1.1957 
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[1m126/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4843 - loss: 1.1731
[1m157/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4865 - loss: 1.1708
[1m190/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4879 - loss: 1.1705
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[1m250/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4898 - loss: 1.1702
[1m282/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4903 - loss: 1.1706
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Epoch 17/28

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[1m 28/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5378 - loss: 1.1226 
[1m 58/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5203 - loss: 1.1455
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Epoch 18/28

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[1m280/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4895 - loss: 1.1616
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Epoch 19/28

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[1m180/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4937 - loss: 1.1695
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[1m238/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4939 - loss: 1.1689
[1m271/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4942 - loss: 1.1685
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[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4951 - loss: 1.1673 - val_accuracy: 0.5523 - val_loss: 1.1349
Epoch 20/28

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[1m 34/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5237 - loss: 1.1075 
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[1m 97/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5192 - loss: 1.1311
[1m123/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5163 - loss: 1.1374
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[1m188/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5113 - loss: 1.1467
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[1m285/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5072 - loss: 1.1516
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Epoch 21/28

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[1m 31/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5245 - loss: 1.1117 
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[1m126/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5050 - loss: 1.1432
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[1m250/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5057 - loss: 1.1469
[1m282/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5058 - loss: 1.1470
[1m313/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.5060 - loss: 1.1470
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Epoch 22/28

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[1m 32/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4880 - loss: 1.1481 
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[1m189/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4983 - loss: 1.1420
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[1m251/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4990 - loss: 1.1409
[1m280/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4993 - loss: 1.1403
[1m313/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.4998 - loss: 1.1399
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Epoch 23/28

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[1m 32/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5169 - loss: 1.1341 
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[1m125/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5092 - loss: 1.1334
[1m158/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5089 - loss: 1.1337
[1m189/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5087 - loss: 1.1333
[1m222/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5084 - loss: 1.1332
[1m251/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5083 - loss: 1.1331
[1m280/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5078 - loss: 1.1339
[1m308/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5076 - loss: 1.1344
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5075 - loss: 1.1346 - val_accuracy: 0.5471 - val_loss: 1.1294
Epoch 24/28

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 24ms/step - accuracy: 0.5312 - loss: 1.0292
[1m 32/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5065 - loss: 1.1070 
[1m 62/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5113 - loss: 1.1055
[1m 93/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5148 - loss: 1.1029
[1m122/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5166 - loss: 1.1023
[1m154/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5167 - loss: 1.1035
[1m184/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5165 - loss: 1.1046
[1m216/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5164 - loss: 1.1052
[1m245/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5161 - loss: 1.1059
[1m274/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5161 - loss: 1.1061
[1m305/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5159 - loss: 1.1065
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5160 - loss: 1.1066 - val_accuracy: 0.5485 - val_loss: 1.1204
Epoch 25/28

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.3750 - loss: 1.4121
[1m 26/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4779 - loss: 1.1635 
[1m 58/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4914 - loss: 1.1429
[1m 89/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4964 - loss: 1.1365
[1m121/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5006 - loss: 1.1312
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Epoch 26/28

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Epoch 27/28

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.4375 - loss: 1.2228
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[1m246/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5131 - loss: 1.1136
[1m278/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5142 - loss: 1.1129
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Epoch 28/28

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4375 - loss: 1.0470
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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m22s[0m 472ms/step2025-11-07 16:57:11.774575: 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_141', 8 bytes spill stores, 8 bytes spill loads


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Saved model to disk.
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Loaded model from disk.
(1545, 3, 250)
(10469, 3, 250)

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7:28[0m 1s/step
[1m 56/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 913us/step
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[1m177/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 858us/step
[1m233/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 870us/step
[1m293/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 864us/step
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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 20ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 18ms/step
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[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m54/89[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 954us/step
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step  
Global accuracy score (validation) = 52.11 [%]
Global F1 score (validation) = 51.01 [%]
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[[0.06818414 0.08186795 0.79487437 0.05507356]
 [0.11281474 0.43128642 0.38748923 0.06840955]
 [0.31740034 0.31340638 0.08200848 0.2871848 ]
 ...
 [0.2284121  0.24362786 0.45741007 0.07054993]
 [0.2538594  0.1620977  0.49874946 0.08529339]
 [0.21560389 0.23845015 0.48591244 0.06003354]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 57.6 [%]
Global accuracy score (test) = 51.78 [%]
Global F1 score (train) = 57.32 [%]
Global F1 score (test) = 50.95 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.43      0.28      0.34       400
MODERATE-INTENSITY       0.52      0.62      0.56       400
         SEDENTARY       0.49      0.68      0.57       400
VIGOROUS-INTENSITY       0.68      0.48      0.56       345

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


Accuracy capturado en la ejecución 12: 51.78 [%]
F1-score capturado en la ejecución 12: 50.95 [%]

=== EJECUCIÓN 13 ===

--- TRAIN (ejecución 13) ---

--- TEST (ejecución 13) ---
2025-11-07 16:57:22.674508: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 16:57:22.685766: 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 00:00:1762531042.698923 3308360 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762531042.703033 3308360 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762531042.712750 3308360 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762531042.712769 3308360 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762531042.712771 3308360 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762531042.712773 3308360 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 16:57:22.715914: 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.
I0000 00:00:1762531044.930579 3308360 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13758 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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Epoch 1/28
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762531047.450622 3308487 service.cc:152] XLA service 0x70c6e4015810 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762531047.450661 3308487 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-07 16:57:27.505683: 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 00:00:1762531047.819618 3308487 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762531050.104580 3308487 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m24:39[0m 5s/step - accuracy: 0.1562 - loss: 2.4114
[1m 25/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2435 - loss: 1.9512  
[1m 54/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2458 - loss: 1.9031
[1m 87/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2543 - loss: 1.8694
[1m116/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2613 - loss: 1.8477
[1m147/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2680 - loss: 1.8293
[1m180/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2735 - loss: 1.8149
[1m213/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2781 - loss: 1.8038
[1m243/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2815 - loss: 1.7950
[1m276/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2848 - loss: 1.7861
[1m309/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.2881 - loss: 1.7774
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Epoch 2/28

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[1m 61/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3748 - loss: 1.5947
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Epoch 3/28

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Epoch 4/28

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[1m 28/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4125 - loss: 1.4114 
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[1m297/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4064 - loss: 1.4205
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Epoch 5/28

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[1m 32/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3865 - loss: 1.4489 
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Epoch 6/28

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Epoch 7/28

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[1m298/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4472 - loss: 1.2813
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Epoch 8/28

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[1m295/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4418 - loss: 1.2891
[1m326/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.4423 - loss: 1.2888
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4424 - loss: 1.2888 - val_accuracy: 0.4975 - val_loss: 1.1627
Epoch 9/28

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[1m 31/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4640 - loss: 1.2561 
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[1m121/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4657 - loss: 1.2526
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[1m184/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4650 - loss: 1.2564
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Epoch 10/28

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[1m180/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4553 - loss: 1.2524
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[1m245/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4583 - loss: 1.2519
[1m275/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4593 - loss: 1.2516
[1m307/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4600 - loss: 1.2514
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Epoch 11/28

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[1m 26/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4700 - loss: 1.2219 
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[1m180/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4632 - loss: 1.2259
[1m211/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4634 - loss: 1.2264
[1m244/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4638 - loss: 1.2263
[1m275/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4640 - loss: 1.2265
[1m307/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4642 - loss: 1.2266
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Epoch 12/28

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5625 - loss: 1.2139
[1m 33/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4551 - loss: 1.2955 
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[1m127/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4571 - loss: 1.2656
[1m158/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4573 - loss: 1.2606
[1m189/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4577 - loss: 1.2568
[1m220/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4588 - loss: 1.2530
[1m251/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4598 - loss: 1.2505
[1m282/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4609 - loss: 1.2479
[1m315/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.4622 - loss: 1.2454
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4626 - loss: 1.2445 - val_accuracy: 0.5021 - val_loss: 1.1409
Epoch 13/28

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4688 - loss: 1.2131
[1m 32/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4750 - loss: 1.2437 
[1m 64/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4714 - loss: 1.2418
[1m 96/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4712 - loss: 1.2373
[1m130/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4713 - loss: 1.2315
[1m163/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4713 - loss: 1.2281
[1m197/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4718 - loss: 1.2257
[1m225/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4725 - loss: 1.2237
[1m251/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4730 - loss: 1.2223
[1m279/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4731 - loss: 1.2214
[1m309/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4730 - loss: 1.2208
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4731 - loss: 1.2203 - val_accuracy: 0.5053 - val_loss: 1.1459
Epoch 14/28

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5625 - loss: 1.2389
[1m 30/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4711 - loss: 1.2407 
[1m 60/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4785 - loss: 1.2172
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Epoch 15/28

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Epoch 16/28

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[1m184/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4825 - loss: 1.1805
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[1m272/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4821 - loss: 1.1797
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Epoch 17/28

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[1m 30/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4753 - loss: 1.2044 
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Epoch 18/28

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[1m114/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5111 - loss: 1.1398
[1m142/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5099 - loss: 1.1409
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[1m202/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5100 - loss: 1.1403
[1m230/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5098 - loss: 1.1403
[1m262/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5097 - loss: 1.1400
[1m291/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5093 - loss: 1.1400
[1m323/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.5089 - loss: 1.1402
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5088 - loss: 1.1402 - val_accuracy: 0.5221 - val_loss: 1.1289
Epoch 19/28

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 25ms/step - accuracy: 0.5625 - loss: 0.9649
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[1m124/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4912 - loss: 1.1500
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[1m186/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4948 - loss: 1.1475
[1m215/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4962 - loss: 1.1466
[1m248/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4977 - loss: 1.1456
[1m279/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4985 - loss: 1.1452
[1m308/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4990 - loss: 1.1449
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Epoch 20/28

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[1m 30/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4983 - loss: 1.1074 
[1m 60/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4972 - loss: 1.1249
[1m 92/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4956 - loss: 1.1340
[1m125/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4956 - loss: 1.1371
[1m155/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4967 - loss: 1.1369
[1m188/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4973 - loss: 1.1371
[1m220/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4978 - loss: 1.1370
[1m253/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4982 - loss: 1.1370
[1m284/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4987 - loss: 1.1368
[1m318/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.4993 - loss: 1.1361
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4994 - loss: 1.1360 - val_accuracy: 0.5270 - val_loss: 1.1201
Epoch 21/28

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.6875 - loss: 0.8865
[1m 28/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5378 - loss: 1.0672 
[1m 61/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5213 - loss: 1.0942
[1m 90/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5162 - loss: 1.1066
[1m120/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5147 - loss: 1.1115
[1m149/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5139 - loss: 1.1150
[1m179/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5134 - loss: 1.1178
[1m208/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5129 - loss: 1.1195
[1m239/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5126 - loss: 1.1206
[1m271/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5122 - loss: 1.1215
[1m302/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5118 - loss: 1.1225
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5117 - loss: 1.1232 - val_accuracy: 0.5235 - val_loss: 1.1270
Epoch 22/28

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5000 - loss: 1.1431
[1m 31/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5055 - loss: 1.1113 
[1m 59/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5105 - loss: 1.1061
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[1m300/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5171 - loss: 1.1077
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Epoch 23/28

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5625 - loss: 1.0278
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[1m245/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5103 - loss: 1.1105
[1m276/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5104 - loss: 1.1110
[1m307/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5107 - loss: 1.1115
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Epoch 24/28

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[1m125/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5181 - loss: 1.1198
[1m157/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5164 - loss: 1.1221
[1m188/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5156 - loss: 1.1234
[1m219/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5151 - loss: 1.1243
[1m250/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5147 - loss: 1.1249
[1m281/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5141 - loss: 1.1256
[1m313/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.5138 - loss: 1.1258
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5137 - loss: 1.1257 - val_accuracy: 0.5277 - val_loss: 1.1166
Epoch 25/28

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.2812 - loss: 1.4960
[1m 32/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4921 - loss: 1.1275 
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[1m 93/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5131 - loss: 1.1072
[1m126/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5144 - loss: 1.1066
[1m157/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5150 - loss: 1.1082
[1m187/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5148 - loss: 1.1096
[1m215/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5146 - loss: 1.1106
[1m243/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5146 - loss: 1.1112
[1m274/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5146 - loss: 1.1115
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Epoch 26/28

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Epoch 27/28

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Epoch 28/28

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[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5331 - loss: 1.0873 - val_accuracy: 0.5270 - val_loss: 1.1157

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m24s[0m 513ms/step2025-11-07 16:57:54.918881: 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_141', 8 bytes spill stores, 8 bytes spill loads


[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 22ms/step  
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 22ms/step
Saved model to disk.
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
Loaded model from disk.
(1545, 3, 250)
(10469, 3, 250)

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[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step  
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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 16ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 18ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 18ms/step

[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m58/89[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 880us/step
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 999us/step
Global accuracy score (validation) = 54.6 [%]
Global F1 score (validation) = 51.23 [%]
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[[0.05935879 0.56538373 0.22830573 0.14695176]
 [0.0376211  0.01762025 0.91684127 0.02791733]
 [0.21470115 0.15188813 0.54228383 0.0911269 ]
 ...
 [0.23676391 0.1612327  0.53674275 0.0652606 ]
 [0.2945125  0.13895775 0.5005599  0.06596979]
 [0.1483591  0.20965333 0.5855455  0.0564421 ]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 57.35 [%]
Global accuracy score (test) = 52.56 [%]
Global F1 score (train) = 55.42 [%]
Global F1 score (test) = 49.43 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.46      0.14      0.21       400
MODERATE-INTENSITY       0.49      0.69      0.57       400
         SEDENTARY       0.50      0.77      0.61       400
VIGOROUS-INTENSITY       0.70      0.50      0.59       345

          accuracy                           0.53      1545
         macro avg       0.54      0.52      0.49      1545
      weighted avg       0.53      0.53      0.49      1545


Accuracy capturado en la ejecución 13: 52.56 [%]
F1-score capturado en la ejecución 13: 49.43 [%]

=== EJECUCIÓN 14 ===

--- TRAIN (ejecución 14) ---

--- TEST (ejecución 14) ---
2025-11-07 16:58:05.895662: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 16:58:05.906974: 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 00:00:1762531085.920293 3312012 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762531085.924270 3312012 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762531085.934241 3312012 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762531085.934262 3312012 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762531085.934264 3312012 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762531085.934265 3312012 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 16:58:05.937395: 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.
I0000 00:00:1762531088.160907 3312012 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13758 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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Epoch 1/28
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762531090.642585 3312144 service.cc:152] XLA service 0x770438029de0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762531090.642645 3312144 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-07 16:58:10.697862: 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 00:00:1762531091.009849 3312144 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762531093.307982 3312144 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m24:34[0m 5s/step - accuracy: 0.2812 - loss: 1.7028
[1m 22/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3020 - loss: 1.7912  
[1m 56/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3058 - loss: 1.7809
[1m 84/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3101 - loss: 1.7677
[1m112/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3151 - loss: 1.7525
[1m144/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3198 - loss: 1.7394
[1m173/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3233 - loss: 1.7295
[1m203/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3265 - loss: 1.7203
[1m232/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3289 - loss: 1.7133
[1m262/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3315 - loss: 1.7064
[1m294/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3341 - loss: 1.6994
[1m324/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.3362 - loss: 1.6935
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step - accuracy: 0.3365 - loss: 1.6928
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m9s[0m 13ms/step - accuracy: 0.3365 - loss: 1.6926 - val_accuracy: 0.4466 - val_loss: 1.3326
Epoch 2/28

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4062 - loss: 1.3898
[1m 32/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3706 - loss: 1.6252 
[1m 63/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3721 - loss: 1.5982
[1m 93/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3768 - loss: 1.5845
[1m125/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3777 - loss: 1.5794
[1m154/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3781 - loss: 1.5754
[1m186/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3783 - loss: 1.5719
[1m217/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3782 - loss: 1.5693
[1m249/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3782 - loss: 1.5664
[1m281/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3786 - loss: 1.5630
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Epoch 3/28

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[1m 31/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3986 - loss: 1.5013 
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[1m 93/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4054 - loss: 1.4899
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[1m216/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4049 - loss: 1.4825
[1m246/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4042 - loss: 1.4810
[1m279/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4036 - loss: 1.4798
[1m308/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4032 - loss: 1.4785
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Epoch 4/28

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[1m183/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4154 - loss: 1.4046
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[1m276/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4149 - loss: 1.4070
[1m303/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4149 - loss: 1.4070
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Epoch 5/28

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[1m 30/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4186 - loss: 1.3963 
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[1m177/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4135 - loss: 1.3771
[1m209/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4147 - loss: 1.3742
[1m239/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4157 - loss: 1.3718
[1m269/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4165 - loss: 1.3699
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Epoch 6/28

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[1m 30/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4484 - loss: 1.3276 
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[1m 95/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4341 - loss: 1.3538
[1m127/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4346 - loss: 1.3529
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[1m187/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4350 - loss: 1.3503
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Epoch 7/28

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Epoch 8/28

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[1m 28/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4778 - loss: 1.2802 
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[1m180/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4492 - loss: 1.3023
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[1m276/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4481 - loss: 1.3005
[1m307/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4482 - loss: 1.2996
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Epoch 9/28

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[1m124/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4394 - loss: 1.2930
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[1m187/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4418 - loss: 1.2898
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[1m250/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4432 - loss: 1.2881
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[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4442 - loss: 1.2876 - val_accuracy: 0.5320 - val_loss: 1.1816
Epoch 10/28

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[1m 32/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4500 - loss: 1.2858 
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[1m 94/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4494 - loss: 1.2788
[1m127/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4505 - loss: 1.2771
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[1m186/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4494 - loss: 1.2786
[1m217/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4487 - loss: 1.2792
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[1m279/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4482 - loss: 1.2784
[1m309/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4483 - loss: 1.2778
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Epoch 11/28

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[1m 31/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4384 - loss: 1.2405 
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Epoch 12/28

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[1m283/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4717 - loss: 1.2382
[1m314/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.4716 - loss: 1.2382
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Epoch 13/28

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[1m 28/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4728 - loss: 1.2014 
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[1m122/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4735 - loss: 1.2220
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[1m185/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4723 - loss: 1.2272
[1m213/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4720 - loss: 1.2280
[1m246/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4718 - loss: 1.2279
[1m278/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4716 - loss: 1.2277
[1m309/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4714 - loss: 1.2275
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4713 - loss: 1.2275 - val_accuracy: 0.5348 - val_loss: 1.1604
Epoch 14/28

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[1m 29/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4810 - loss: 1.1516 
[1m 59/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4654 - loss: 1.1872
[1m 93/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4614 - loss: 1.2007
[1m125/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4600 - loss: 1.2064
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[1m190/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4614 - loss: 1.2083
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[1m250/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4623 - loss: 1.2086
[1m279/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4628 - loss: 1.2089
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Epoch 15/28

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[1m 29/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4581 - loss: 1.1948 
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Epoch 16/28

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Epoch 17/28

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Epoch 18/28

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[1m 29/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4707 - loss: 1.1696 
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Epoch 19/28

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[1m 29/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4707 - loss: 1.2225 
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[1m 91/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4781 - loss: 1.2072
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[1m184/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4842 - loss: 1.1917
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[1m243/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4872 - loss: 1.1847
[1m274/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4883 - loss: 1.1824
[1m304/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4888 - loss: 1.1809
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Epoch 20/28

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[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4943 - loss: 1.1532
[1m301/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4946 - loss: 1.1533
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Epoch 21/28

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[1m 32/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4767 - loss: 1.1860 
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[1m128/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4868 - loss: 1.1652
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[1m191/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4884 - loss: 1.1642
[1m220/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4895 - loss: 1.1633
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[1m282/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4918 - loss: 1.1611
[1m312/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.4927 - loss: 1.1603
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4931 - loss: 1.1599 - val_accuracy: 0.5365 - val_loss: 1.1533
Epoch 22/28

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5000 - loss: 1.1708
[1m 30/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5078 - loss: 1.1381 
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[1m 92/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5041 - loss: 1.1378
[1m122/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5013 - loss: 1.1417
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[1m183/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5011 - loss: 1.1419
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[1m247/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5013 - loss: 1.1415
[1m276/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5014 - loss: 1.1413
[1m307/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5015 - loss: 1.1412
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5015 - loss: 1.1412 - val_accuracy: 0.5393 - val_loss: 1.1480
Epoch 23/28

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[1m 30/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5109 - loss: 1.1211 
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[1m 89/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5149 - loss: 1.1244
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[1m309/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5145 - loss: 1.1263
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Epoch 24/28

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[1m277/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5128 - loss: 1.1289
[1m306/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5120 - loss: 1.1296
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Epoch 25/28

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[1m 32/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5284 - loss: 1.1427 
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[1m159/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5203 - loss: 1.1312
[1m190/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5193 - loss: 1.1313
[1m220/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5188 - loss: 1.1307
[1m248/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5184 - loss: 1.1300
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[1m314/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.5180 - loss: 1.1283
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5179 - loss: 1.1280 - val_accuracy: 0.5460 - val_loss: 1.1466
Epoch 26/28

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.4688 - loss: 1.1837
[1m 30/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5140 - loss: 1.1463 
[1m 62/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5087 - loss: 1.1376
[1m 93/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5088 - loss: 1.1318
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[1m186/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5096 - loss: 1.1255
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Epoch 27/28

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[1m 31/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5061 - loss: 1.1365 
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[1m125/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5146 - loss: 1.1234
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[1m190/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5157 - loss: 1.1220
[1m223/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5158 - loss: 1.1214
[1m255/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5158 - loss: 1.1208
[1m286/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5158 - loss: 1.1205
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Epoch 28/28

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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m23s[0m 491ms/step2025-11-07 16:58:37.917572: 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_141', 8 bytes spill stores, 8 bytes spill loads


[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 21ms/step  
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Saved model to disk.
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Loaded model from disk.
(1545, 3, 250)
(10469, 3, 250)

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[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 19ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 20ms/step

[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 15ms/step
[1m50/89[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step 
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step
Global accuracy score (validation) = 52.18 [%]
Global F1 score (validation) = 51.36 [%]
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[[0.1820393  0.05356779 0.57649267 0.18790027]
 [0.22473966 0.05531991 0.53954536 0.18039513]
 [0.32949972 0.28227928 0.0799028  0.3083182 ]
 ...
 [0.33997825 0.11251234 0.46488962 0.08261977]
 [0.32509342 0.07857868 0.42994398 0.16638385]
 [0.2930312  0.16510339 0.4495495  0.09231596]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 56.26 [%]
Global accuracy score (test) = 47.64 [%]
Global F1 score (train) = 55.57 [%]
Global F1 score (test) = 47.4 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.37      0.44      0.40       400
MODERATE-INTENSITY       0.52      0.32      0.40       400
         SEDENTARY       0.49      0.66      0.56       400
VIGOROUS-INTENSITY       0.59      0.48      0.53       345

          accuracy                           0.48      1545
         macro avg       0.49      0.48      0.47      1545
      weighted avg       0.49      0.48      0.47      1545


Accuracy capturado en la ejecución 14: 47.64 [%]
F1-score capturado en la ejecución 14: 47.4 [%]

=== EJECUCIÓN 15 ===

--- TRAIN (ejecución 15) ---

--- TEST (ejecución 15) ---
2025-11-07 16:58:48.910188: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 16:58:48.921595: 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 00:00:1762531128.934586 3315696 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762531128.938522 3315696 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762531128.948503 3315696 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762531128.948521 3315696 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762531128.948524 3315696 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762531128.948525 3315696 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 16:58:48.951494: 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.
I0000 00:00:1762531131.178318 3315696 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13758 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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Epoch 1/28
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762531133.636788 3315828 service.cc:152] XLA service 0x706bc0013600 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762531133.636822 3315828 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-07 16:58:53.685963: 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 00:00:1762531134.000981 3315828 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762531136.309762 3315828 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 2/28

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[1m175/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3485 - loss: 1.6249
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Epoch 3/28

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Epoch 4/28

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Epoch 5/28

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Epoch 6/28

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Epoch 7/28

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Epoch 8/28

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[1m 28/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4305 - loss: 1.2940 
[1m 58/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4226 - loss: 1.3276
[1m 89/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4241 - loss: 1.3295
[1m121/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4270 - loss: 1.3273
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[1m246/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4324 - loss: 1.3218
[1m279/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4334 - loss: 1.3203
[1m309/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4341 - loss: 1.3189
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Epoch 9/28

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[1m 32/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4458 - loss: 1.2468 
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[1m 96/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4523 - loss: 1.2611
[1m126/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4534 - loss: 1.2628
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[1m189/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4540 - loss: 1.2662
[1m216/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4539 - loss: 1.2673
[1m246/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4539 - loss: 1.2678
[1m276/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4537 - loss: 1.2684
[1m301/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4535 - loss: 1.2690
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4533 - loss: 1.2694 - val_accuracy: 0.5214 - val_loss: 1.1334
Epoch 10/28

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[1m 29/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4852 - loss: 1.2637 
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[1m119/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4627 - loss: 1.2832
[1m149/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4620 - loss: 1.2807
[1m179/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4620 - loss: 1.2779
[1m205/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4620 - loss: 1.2758
[1m235/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4618 - loss: 1.2740
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[1m293/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4613 - loss: 1.2719
[1m324/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.4611 - loss: 1.2709
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4612 - loss: 1.2708 - val_accuracy: 0.5081 - val_loss: 1.1379
Epoch 11/28

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[1m 29/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4511 - loss: 1.2474 
[1m 56/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4551 - loss: 1.2509
[1m 88/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4608 - loss: 1.2459
[1m117/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4637 - loss: 1.2421
[1m147/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4657 - loss: 1.2400
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[1m272/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4700 - loss: 1.2356
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Epoch 12/28

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[1m 30/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4674 - loss: 1.2335 
[1m 58/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4622 - loss: 1.2399
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[1m292/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4646 - loss: 1.2444
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Epoch 13/28

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[1m 31/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5285 - loss: 1.1931 
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[1m299/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4846 - loss: 1.2179
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Epoch 14/28

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[1m 32/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4648 - loss: 1.2412 
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[1m124/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4715 - loss: 1.2105
[1m158/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4729 - loss: 1.2061
[1m186/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4736 - loss: 1.2042
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[1m282/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4751 - loss: 1.2020
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Epoch 15/28

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[1m 29/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5526 - loss: 1.1076 
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Epoch 16/28

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Epoch 17/28

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Epoch 18/28

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Epoch 19/28

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[1m 28/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5137 - loss: 1.1414 
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Epoch 20/28

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[1m 33/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4876 - loss: 1.1490 
[1m 65/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4875 - loss: 1.1492
[1m 93/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4868 - loss: 1.1508
[1m125/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4880 - loss: 1.1487
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[1m181/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4908 - loss: 1.1470
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[1m244/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4935 - loss: 1.1460
[1m275/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4945 - loss: 1.1461
[1m307/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4956 - loss: 1.1459
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Epoch 21/28

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[1m 30/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5208 - loss: 1.1235 
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[1m 90/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5170 - loss: 1.1249
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[1m181/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5124 - loss: 1.1304
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[1m242/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5095 - loss: 1.1334
[1m271/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5089 - loss: 1.1339
[1m301/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5085 - loss: 1.1341
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5083 - loss: 1.1343 - val_accuracy: 0.5270 - val_loss: 1.1118
Epoch 22/28

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4375 - loss: 1.2107
[1m 29/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4830 - loss: 1.1565 
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[1m 94/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5047 - loss: 1.1355
[1m126/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5054 - loss: 1.1367
[1m153/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5058 - loss: 1.1375
[1m182/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5066 - loss: 1.1376
[1m213/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5073 - loss: 1.1369
[1m244/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5080 - loss: 1.1361
[1m274/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5084 - loss: 1.1354
[1m305/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5088 - loss: 1.1347
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5089 - loss: 1.1345 - val_accuracy: 0.5221 - val_loss: 1.1077
Epoch 23/28

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 23ms/step - accuracy: 0.4688 - loss: 1.1864
[1m 31/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4956 - loss: 1.1389 
[1m 61/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5044 - loss: 1.1332
[1m 91/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5077 - loss: 1.1304
[1m121/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5094 - loss: 1.1283
[1m152/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5101 - loss: 1.1273
[1m184/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5100 - loss: 1.1265
[1m217/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5098 - loss: 1.1260
[1m247/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5099 - loss: 1.1254
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Epoch 24/28

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[1m 30/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5211 - loss: 1.1046 
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[1m273/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5208 - loss: 1.1116
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Epoch 25/28

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Epoch 26/28

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[1m125/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5290 - loss: 1.0854
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[1m215/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5286 - loss: 1.0868
[1m248/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5282 - loss: 1.0878
[1m281/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5280 - loss: 1.0883
[1m315/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.5275 - loss: 1.0892
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5273 - loss: 1.0895 - val_accuracy: 0.5214 - val_loss: 1.1110
Epoch 27/28

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[1m 31/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5236 - loss: 1.1130 
[1m 60/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5217 - loss: 1.1127
[1m 89/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5228 - loss: 1.1100
[1m118/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5241 - loss: 1.1079
[1m147/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5252 - loss: 1.1055
[1m178/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5261 - loss: 1.1025
[1m209/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5263 - loss: 1.1004
[1m238/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5264 - loss: 1.0989
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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m23s[0m 492ms/step2025-11-07 16:59:20.513885: 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_141', 8 bytes spill stores, 8 bytes spill loads

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Saved model to disk.
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Loaded model from disk.
(1545, 3, 250)
(10469, 3, 250)

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6:58[0m 1s/step
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[1m55/89[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 932us/step
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step  
Global accuracy score (validation) = 53.62 [%]
Global F1 score (validation) = 49.75 [%]
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[[0.21407478 0.16863456 0.5146955  0.10259517]
 [0.17293103 0.25509894 0.4688915  0.10307854]
 [0.1906739  0.12296698 0.5440722  0.14228687]
 ...
 [0.20821787 0.22714373 0.48651126 0.07812711]
 [0.23376496 0.21829455 0.43272763 0.11521285]
 [0.13952927 0.35434693 0.45193887 0.0541849 ]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 56.79 [%]
Global accuracy score (test) = 50.81 [%]
Global F1 score (train) = 54.68 [%]
Global F1 score (test) = 47.65 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.40      0.13      0.20       400
MODERATE-INTENSITY       0.47      0.69      0.56       400
         SEDENTARY       0.51      0.74      0.60       400
VIGOROUS-INTENSITY       0.66      0.47      0.55       345

          accuracy                           0.51      1545
         macro avg       0.51      0.51      0.48      1545
      weighted avg       0.50      0.51      0.47      1545


Accuracy capturado en la ejecución 15: 50.81 [%]
F1-score capturado en la ejecución 15: 47.65 [%]

=== EJECUCIÓN 16 ===

--- TRAIN (ejecución 16) ---

--- TEST (ejecución 16) ---
2025-11-07 16:59:31.652246: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 16:59:31.663756: 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 00:00:1762531171.676929 3319270 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762531171.680905 3319270 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762531171.690837 3319270 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762531171.690857 3319270 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762531171.690859 3319270 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762531171.690860 3319270 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 16:59:31.694048: 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.
I0000 00:00:1762531173.942997 3319270 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13760 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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Epoch 1/28
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762531176.462192 3319400 service.cc:152] XLA service 0x7b05ec027f00 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762531176.462229 3319400 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-07 16:59:36.513861: 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 00:00:1762531176.837274 3319400 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762531179.118246 3319400 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m24:34[0m 5s/step - accuracy: 0.2188 - loss: 1.8151
[1m 25/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2612 - loss: 1.8407  
[1m 58/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2700 - loss: 1.8330
[1m 89/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2742 - loss: 1.8189
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[1m149/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2853 - loss: 1.7934
[1m181/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2902 - loss: 1.7826
[1m214/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2941 - loss: 1.7731
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Epoch 2/28

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Epoch 3/28

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Epoch 4/28

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Epoch 5/28

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[1m 28/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3977 - loss: 1.4098 
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Epoch 6/28

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[1m 29/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4350 - loss: 1.3807 
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[1m 92/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4381 - loss: 1.3442
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[1m246/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4400 - loss: 1.3366
[1m276/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4398 - loss: 1.3364
[1m308/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4397 - loss: 1.3361
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Epoch 7/28

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[1m279/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4348 - loss: 1.3212
[1m310/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4351 - loss: 1.3211
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Epoch 8/28

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[1m 31/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4767 - loss: 1.3125 
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[1m181/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4512 - loss: 1.3071
[1m208/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4499 - loss: 1.3068
[1m240/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4486 - loss: 1.3065
[1m271/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4478 - loss: 1.3057
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Epoch 9/28

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[1m 33/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4528 - loss: 1.3283 
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Epoch 10/28

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[1m 28/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4727 - loss: 1.3138 
[1m 59/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4657 - loss: 1.3074
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[1m118/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4617 - loss: 1.2970
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[1m239/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4598 - loss: 1.2830
[1m266/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4601 - loss: 1.2805
[1m296/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4602 - loss: 1.2782
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Epoch 11/28

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[1m 32/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4394 - loss: 1.2269 
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[1m273/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4568 - loss: 1.2346
[1m301/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4571 - loss: 1.2346
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Epoch 12/28

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[1m 33/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4400 - loss: 1.1933 
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[1m182/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4549 - loss: 1.2139
[1m210/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4562 - loss: 1.2145
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[1m267/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4586 - loss: 1.2154
[1m299/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4599 - loss: 1.2155
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4610 - loss: 1.2155 - val_accuracy: 0.5070 - val_loss: 1.1723
Epoch 13/28

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[1m 33/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4611 - loss: 1.1919 
[1m 61/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4611 - loss: 1.2019
[1m 91/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4622 - loss: 1.2068
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[1m273/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4694 - loss: 1.2132
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Epoch 14/28

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[1m 31/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4857 - loss: 1.2182 
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[1m274/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4743 - loss: 1.2091
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Epoch 15/28

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Epoch 16/28

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Epoch 17/28

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[1m 29/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4938 - loss: 1.1355 
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Epoch 18/28

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[1m 30/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4581 - loss: 1.2087 
[1m 62/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4658 - loss: 1.1840
[1m 92/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4709 - loss: 1.1739
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[1m180/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4763 - loss: 1.1684
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[1m241/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4795 - loss: 1.1653
[1m272/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4808 - loss: 1.1642
[1m303/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4822 - loss: 1.1631
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Epoch 19/28

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[1m 32/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5141 - loss: 1.1391 
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[1m 93/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5032 - loss: 1.1469
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[1m183/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5008 - loss: 1.1487
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[1m246/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4997 - loss: 1.1500
[1m279/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4993 - loss: 1.1501
[1m309/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4990 - loss: 1.1499
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Epoch 20/28

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[1m 30/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4940 - loss: 1.1136 
[1m 58/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4931 - loss: 1.1222
[1m 88/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4906 - loss: 1.1342
[1m119/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4892 - loss: 1.1433
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[1m181/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4892 - loss: 1.1504
[1m210/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4901 - loss: 1.1508
[1m238/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4906 - loss: 1.1513
[1m272/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4914 - loss: 1.1513
[1m300/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4922 - loss: 1.1509
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4931 - loss: 1.1504 - val_accuracy: 0.5253 - val_loss: 1.1584
Epoch 21/28

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[1m 30/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5056 - loss: 1.1309 
[1m 62/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4997 - loss: 1.1418
[1m 93/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4971 - loss: 1.1465
[1m124/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4964 - loss: 1.1472
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[1m187/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4983 - loss: 1.1465
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[1m247/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4996 - loss: 1.1452
[1m272/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4998 - loss: 1.1448
[1m303/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5003 - loss: 1.1440
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Epoch 22/28

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[1m 29/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5160 - loss: 1.1037 
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[1m182/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5196 - loss: 1.1005
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[1m308/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5200 - loss: 1.1016
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Epoch 23/28

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[1m292/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5152 - loss: 1.1157
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Epoch 24/28

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5312 - loss: 1.0688
[1m 25/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5123 - loss: 1.0843 
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[1m115/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5127 - loss: 1.1008
[1m142/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5134 - loss: 1.1017
[1m173/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5142 - loss: 1.1028
[1m203/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5151 - loss: 1.1034
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[1m261/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5159 - loss: 1.1049
[1m290/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5159 - loss: 1.1059
[1m321/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.5159 - loss: 1.1068
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5159 - loss: 1.1070 - val_accuracy: 0.5126 - val_loss: 1.1508
Epoch 25/28

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.4688 - loss: 1.0782
[1m 30/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5406 - loss: 1.0705 
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[1m 97/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5326 - loss: 1.0844
[1m128/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5292 - loss: 1.0888
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[1m191/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5249 - loss: 1.0953
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[1m284/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5223 - loss: 1.1008
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Epoch 26/28

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Epoch 27/28

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.3750 - loss: 1.1764
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Epoch 28/28

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4062 - loss: 1.2073
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[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5161 - loss: 1.0951 - val_accuracy: 0.5197 - val_loss: 1.1456

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m23s[0m 484ms/step2025-11-07 17:00:04.044395: 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_141', 8 bytes spill stores, 8 bytes spill loads


[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 21ms/step  
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 22ms/step
Saved model to disk.
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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Loaded model from disk.
(1545, 3, 250)
(10469, 3, 250)

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[1m282/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 902us/step
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step  
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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 16ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 19ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 19ms/step

[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 15ms/step
[1m51/89[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step 
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step
Global accuracy score (validation) = 52.07 [%]
Global F1 score (validation) = 51.09 [%]
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[[0.22452575 0.18746388 0.5026452  0.08536511]
 [0.52517116 0.19796488 0.26981783 0.00704611]
 [0.52863234 0.19794355 0.26641992 0.00700413]
 ...
 [0.3193755  0.15894108 0.45278412 0.06889933]
 [0.26331273 0.15007862 0.49059343 0.09601519]
 [0.2609149  0.1725233  0.5244586  0.04210322]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 56.62 [%]
Global accuracy score (test) = 48.16 [%]
Global F1 score (train) = 56.3 [%]
Global F1 score (test) = 47.64 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.36      0.35      0.35       400
MODERATE-INTENSITY       0.48      0.39      0.43       400
         SEDENTARY       0.52      0.72      0.60       400
VIGOROUS-INTENSITY       0.60      0.46      0.52       345

          accuracy                           0.48      1545
         macro avg       0.49      0.48      0.48      1545
      weighted avg       0.48      0.48      0.47      1545


Accuracy capturado en la ejecución 16: 48.16 [%]
F1-score capturado en la ejecución 16: 47.64 [%]

=== EJECUCIÓN 17 ===

--- TRAIN (ejecución 17) ---

--- TEST (ejecución 17) ---
2025-11-07 17:00:14.994975: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 17:00:15.006442: 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 00:00:1762531215.020154 3322946 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762531215.024106 3322946 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762531215.034095 3322946 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762531215.034115 3322946 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762531215.034117 3322946 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762531215.034119 3322946 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 17:00:15.037244: 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.
I0000 00:00:1762531217.254235 3322946 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13760 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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Epoch 1/28
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762531219.702278 3323075 service.cc:152] XLA service 0x7d5adc002750 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762531219.702313 3323075 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-07 17:00:19.751937: 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 00:00:1762531220.065237 3323075 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762531222.383906 3323075 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m24:31[0m 4s/step - accuracy: 0.1875 - loss: 2.0056
[1m 30/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2712 - loss: 1.8891  
[1m 63/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2809 - loss: 1.8468
[1m 97/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2842 - loss: 1.8263
[1m130/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2877 - loss: 1.8118
[1m161/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2914 - loss: 1.7983
[1m192/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2948 - loss: 1.7855
[1m224/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2977 - loss: 1.7736
[1m254/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3006 - loss: 1.7632
[1m287/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3034 - loss: 1.7538
[1m317/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.3058 - loss: 1.7456
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step - accuracy: 0.3067 - loss: 1.7428
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m9s[0m 12ms/step - accuracy: 0.3068 - loss: 1.7426 - val_accuracy: 0.4607 - val_loss: 1.2711
Epoch 2/28

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[1m 33/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3517 - loss: 1.6481 
[1m 62/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3609 - loss: 1.6249
[1m 93/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3634 - loss: 1.6078
[1m125/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3654 - loss: 1.5945
[1m156/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3667 - loss: 1.5853
[1m187/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3676 - loss: 1.5782
[1m217/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3680 - loss: 1.5730
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[1m276/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3694 - loss: 1.5633
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Epoch 3/28

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Epoch 4/28

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Epoch 5/28

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Epoch 6/28

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[1m 33/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4225 - loss: 1.3620 
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Epoch 7/28

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[1m 31/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4211 - loss: 1.3537 
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[1m308/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4404 - loss: 1.3218
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Epoch 8/28

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[1m 29/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4795 - loss: 1.2874 
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[1m298/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4459 - loss: 1.2940
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Epoch 9/28

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[1m186/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4659 - loss: 1.2590
[1m215/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4639 - loss: 1.2601
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Epoch 10/28

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[1m 31/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4527 - loss: 1.3115 
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Epoch 11/28

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[1m 31/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4710 - loss: 1.2010 
[1m 61/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4612 - loss: 1.2239
[1m 92/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4584 - loss: 1.2306
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[1m242/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4536 - loss: 1.2423
[1m272/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4532 - loss: 1.2434
[1m302/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4533 - loss: 1.2439
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Epoch 12/28

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[1m 26/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4839 - loss: 1.2174 
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[1m 87/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4829 - loss: 1.2162
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[1m186/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4833 - loss: 1.2141
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Epoch 13/28

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[1m 29/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4865 - loss: 1.2287 
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[1m187/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4776 - loss: 1.2149
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[1m247/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4754 - loss: 1.2159
[1m278/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4748 - loss: 1.2157
[1m309/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4745 - loss: 1.2154
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4745 - loss: 1.2150 - val_accuracy: 0.5116 - val_loss: 1.1655
Epoch 14/28

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[1m 31/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4614 - loss: 1.2669 
[1m 62/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4737 - loss: 1.2294
[1m 93/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4764 - loss: 1.2201
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[1m297/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4796 - loss: 1.2016
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Epoch 15/28

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[1m 32/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4824 - loss: 1.1899 
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Epoch 16/28

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Epoch 17/28

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[1m245/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4790 - loss: 1.1743
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Epoch 18/28

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[1m 29/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4979 - loss: 1.1657 
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Epoch 19/28

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[1m 31/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4953 - loss: 1.1477 
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[1m187/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5030 - loss: 1.1358
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Epoch 20/28

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[1m280/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4962 - loss: 1.1442
[1m308/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4971 - loss: 1.1432
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Epoch 21/28

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[1m182/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5089 - loss: 1.1495
[1m214/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5083 - loss: 1.1467
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[1m311/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5074 - loss: 1.1419
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5074 - loss: 1.1412 - val_accuracy: 0.5263 - val_loss: 1.1326
Epoch 22/28

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5000 - loss: 1.2994
[1m 30/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4777 - loss: 1.1866 
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[1m 92/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4950 - loss: 1.1581
[1m122/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4974 - loss: 1.1530
[1m153/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4988 - loss: 1.1503
[1m183/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4998 - loss: 1.1481
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[1m243/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5013 - loss: 1.1436
[1m273/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5017 - loss: 1.1419
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Epoch 23/28

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[1m 28/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5426 - loss: 1.0656 
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Epoch 24/28

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[1m267/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5268 - loss: 1.0967
[1m300/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5255 - loss: 1.0983
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Epoch 25/28

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[1m124/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4950 - loss: 1.1117
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[1m184/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5008 - loss: 1.1072
[1m217/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5024 - loss: 1.1056
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[1m277/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5044 - loss: 1.1037
[1m307/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5052 - loss: 1.1033
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5056 - loss: 1.1031 - val_accuracy: 0.5123 - val_loss: 1.1292
Epoch 26/28

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[1m 29/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5510 - loss: 1.0615 
[1m 59/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5375 - loss: 1.0756
[1m 90/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5293 - loss: 1.0846
[1m118/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5250 - loss: 1.0886
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[1m180/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5203 - loss: 1.0936
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[1m272/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5196 - loss: 1.0947
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Epoch 27/28

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 27ms/step - accuracy: 0.4062 - loss: 1.2138
[1m 32/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5032 - loss: 1.0984 
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[1m 91/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5090 - loss: 1.0917
[1m121/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5105 - loss: 1.0909
[1m150/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5112 - loss: 1.0894
[1m181/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5117 - loss: 1.0884
[1m212/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5125 - loss: 1.0883
[1m239/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5133 - loss: 1.0880
[1m269/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5143 - loss: 1.0876
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Epoch 28/28

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4375 - loss: 1.1182
[1m 29/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5090 - loss: 1.0622 
[1m 62/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5213 - loss: 1.0588
[1m 92/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5227 - loss: 1.0652
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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m23s[0m 479ms/step2025-11-07 17:00:47.054568: 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_141', 8 bytes spill stores, 8 bytes spill loads


[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 21ms/step  
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 22ms/step
Saved model to disk.
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Loaded model from disk.
(1545, 3, 250)
(10469, 3, 250)

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7:11[0m 1s/step
[1m 57/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 900us/step
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[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 19ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 19ms/step

[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 15ms/step
[1m60/89[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 855us/step
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 983us/step
Global accuracy score (validation) = 52.07 [%]
Global F1 score (validation) = 48.73 [%]
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[[0.36887866 0.27171963 0.3484608  0.01094095]
 [0.09798139 0.3603722  0.39027417 0.1513722 ]
 [0.15247855 0.07209382 0.6569514  0.11847626]
 ...
 [0.2581103  0.16940638 0.46753326 0.10495003]
 [0.2580089  0.15611102 0.47946557 0.10641452]
 [0.26805538 0.28007558 0.36826697 0.08360206]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 57.19 [%]
Global accuracy score (test) = 49.39 [%]
Global F1 score (train) = 55.28 [%]
Global F1 score (test) = 46.26 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.33      0.11      0.17       400
MODERATE-INTENSITY       0.46      0.69      0.55       400
         SEDENTARY       0.51      0.68      0.58       400
VIGOROUS-INTENSITY       0.62      0.50      0.55       345

          accuracy                           0.49      1545
         macro avg       0.48      0.49      0.46      1545
      weighted avg       0.47      0.49      0.46      1545


Accuracy capturado en la ejecución 17: 49.39 [%]
F1-score capturado en la ejecución 17: 46.26 [%]

=== EJECUCIÓN 18 ===

--- TRAIN (ejecución 18) ---

--- TEST (ejecución 18) ---
2025-11-07 17:00:58.060150: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 17:00:58.071572: 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 00:00:1762531258.085169 3326601 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762531258.089174 3326601 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762531258.099337 3326601 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762531258.099357 3326601 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762531258.099359 3326601 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762531258.099361 3326601 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 17:00:58.102575: 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.
I0000 00:00:1762531260.360917 3326601 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13760 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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Epoch 1/28
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762531262.863991 3326733 service.cc:152] XLA service 0x7e0c480177b0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762531262.864048 3326733 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-07 17:01:02.922683: 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 00:00:1762531263.237343 3326733 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762531265.523858 3326733 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 2/28

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[1m153/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3817 - loss: 1.5807
[1m185/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3807 - loss: 1.5788
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[1m245/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3798 - loss: 1.5754
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Epoch 3/28

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Epoch 4/28

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Epoch 5/28

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Epoch 6/28

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Epoch 7/28

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[1m 89/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4185 - loss: 1.3450
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[1m180/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4190 - loss: 1.3560
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Epoch 8/28

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[1m192/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4445 - loss: 1.3035
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[1m257/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4443 - loss: 1.3059
[1m288/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4443 - loss: 1.3062
[1m318/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.4443 - loss: 1.3063
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Epoch 9/28

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[1m185/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4482 - loss: 1.3071
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[1m246/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4501 - loss: 1.3019
[1m274/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4505 - loss: 1.3006
[1m302/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4509 - loss: 1.2998
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Epoch 10/28

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[1m 31/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4713 - loss: 1.2582 
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[1m123/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4592 - loss: 1.2688
[1m153/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4584 - loss: 1.2685
[1m184/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4576 - loss: 1.2695
[1m213/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4574 - loss: 1.2697
[1m246/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4570 - loss: 1.2702
[1m277/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4567 - loss: 1.2701
[1m307/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4565 - loss: 1.2701
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4565 - loss: 1.2699 - val_accuracy: 0.4905 - val_loss: 1.1709
Epoch 11/28

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[1m 33/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4402 - loss: 1.2968 
[1m 65/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4460 - loss: 1.2833
[1m 96/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4480 - loss: 1.2774
[1m126/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4475 - loss: 1.2760
[1m158/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4477 - loss: 1.2738
[1m185/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4480 - loss: 1.2721
[1m214/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4488 - loss: 1.2702
[1m247/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4500 - loss: 1.2677
[1m276/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4510 - loss: 1.2657
[1m304/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4518 - loss: 1.2642
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4523 - loss: 1.2631 - val_accuracy: 0.4902 - val_loss: 1.1747
Epoch 12/28

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.2812 - loss: 1.5903
[1m 28/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4483 - loss: 1.2441 
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[1m239/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4641 - loss: 1.2399
[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4654 - loss: 1.2379
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Epoch 13/28

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[1m191/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4625 - loss: 1.2495
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[1m256/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4634 - loss: 1.2457
[1m286/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4640 - loss: 1.2437
[1m319/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.4645 - loss: 1.2420
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Epoch 14/28

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[1m 89/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4814 - loss: 1.2005
[1m120/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4787 - loss: 1.2049
[1m150/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4780 - loss: 1.2057
[1m182/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4772 - loss: 1.2075
[1m213/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4770 - loss: 1.2080
[1m245/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4769 - loss: 1.2086
[1m274/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4769 - loss: 1.2090
[1m307/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4770 - loss: 1.2094
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4770 - loss: 1.2098 - val_accuracy: 0.4810 - val_loss: 1.1668
Epoch 15/28

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 24ms/step - accuracy: 0.3750 - loss: 1.5918
[1m 30/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4567 - loss: 1.2488 
[1m 59/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4703 - loss: 1.2338
[1m 91/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4771 - loss: 1.2208
[1m121/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4787 - loss: 1.2170
[1m152/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4803 - loss: 1.2128
[1m184/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4808 - loss: 1.2104
[1m214/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4813 - loss: 1.2080
[1m247/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4814 - loss: 1.2063
[1m278/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4815 - loss: 1.2046
[1m309/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4817 - loss: 1.2031
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4819 - loss: 1.2025 - val_accuracy: 0.4965 - val_loss: 1.1522
Epoch 16/28

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.3125 - loss: 1.3800
[1m 29/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4792 - loss: 1.2131 
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[1m243/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4728 - loss: 1.2155
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Epoch 17/28

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[1m280/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4771 - loss: 1.2023
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Epoch 18/28

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[1m191/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4760 - loss: 1.1974
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[1m279/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4809 - loss: 1.1889
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Epoch 19/28

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[1m 32/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5475 - loss: 1.0949 
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[1m 96/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5336 - loss: 1.1194
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[1m191/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5214 - loss: 1.1365
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[1m285/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5161 - loss: 1.1436
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Epoch 20/28

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[1m 33/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5369 - loss: 1.0852 
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[1m 98/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5200 - loss: 1.1211
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[1m255/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5137 - loss: 1.1393
[1m285/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5127 - loss: 1.1410
[1m314/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.5117 - loss: 1.1424
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Epoch 21/28

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.6250 - loss: 0.8417
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[1m184/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5076 - loss: 1.1414
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[1m247/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5065 - loss: 1.1414
[1m277/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5062 - loss: 1.1415
[1m308/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5057 - loss: 1.1420
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Epoch 22/28

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[1m 30/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4916 - loss: 1.1538 
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[1m 89/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5027 - loss: 1.1299
[1m120/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5042 - loss: 1.1261
[1m152/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5057 - loss: 1.1235
[1m185/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5070 - loss: 1.1221
[1m214/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5078 - loss: 1.1218
[1m246/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5083 - loss: 1.1221
[1m280/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5087 - loss: 1.1225
[1m310/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5089 - loss: 1.1229
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5090 - loss: 1.1231 - val_accuracy: 0.5130 - val_loss: 1.1247
Epoch 23/28

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.3438 - loss: 1.4796
[1m 28/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4949 - loss: 1.1651 
[1m 61/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5096 - loss: 1.1303
[1m 87/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5145 - loss: 1.1204
[1m120/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5159 - loss: 1.1173
[1m151/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5150 - loss: 1.1187
[1m177/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5141 - loss: 1.1196
[1m209/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5139 - loss: 1.1198
[1m242/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5138 - loss: 1.1201
[1m273/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5136 - loss: 1.1206
[1m301/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5134 - loss: 1.1212
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5132 - loss: 1.1218 - val_accuracy: 0.5197 - val_loss: 1.1230
Epoch 24/28

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4375 - loss: 1.2184
[1m 33/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4846 - loss: 1.1067 
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Epoch 25/28

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[1m275/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5071 - loss: 1.1313
[1m305/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5082 - loss: 1.1300
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Epoch 26/28

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[1m121/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5105 - loss: 1.1030
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[1m182/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5123 - loss: 1.1046
[1m208/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5127 - loss: 1.1058
[1m240/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5132 - loss: 1.1068
[1m272/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5135 - loss: 1.1079
[1m302/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5137 - loss: 1.1089
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5139 - loss: 1.1093 - val_accuracy: 0.4982 - val_loss: 1.1362
Epoch 27/28

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[1m 30/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5365 - loss: 1.0984 
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[1m 92/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5321 - loss: 1.0945
[1m124/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5314 - loss: 1.0954
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[1m188/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5293 - loss: 1.0970
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[1m281/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5270 - loss: 1.0985
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Epoch 28/28

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[1m 31/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5061 - loss: 1.1258 
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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m23s[0m 486ms/step2025-11-07 17:01:30.136724: 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_141', 8 bytes spill stores, 8 bytes spill loads

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Saved model to disk.
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Loaded model from disk.
(1545, 3, 250)
(10469, 3, 250)

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6:57[0m 1s/step
[1m 53/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 973us/step
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[1m172/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 884us/step
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[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 15ms/step
[1m59/89[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 865us/step
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 985us/step
Global accuracy score (validation) = 52.18 [%]
Global F1 score (validation) = 50.02 [%]
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[[0.04923116 0.03181148 0.8977048  0.02125248]
 [0.24774134 0.51181424 0.16392408 0.0765204 ]
 [0.24070917 0.07548071 0.59603953 0.08777051]
 ...
 [0.25662193 0.09499059 0.5934819  0.05490559]
 [0.24326946 0.13008232 0.5086776  0.11797062]
 [0.22923703 0.15352157 0.5742493  0.04299212]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 56.91 [%]
Global accuracy score (test) = 48.09 [%]
Global F1 score (train) = 56.16 [%]
Global F1 score (test) = 46.72 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.39      0.22      0.28       400
MODERATE-INTENSITY       0.50      0.51      0.51       400
         SEDENTARY       0.45      0.71      0.55       400
VIGOROUS-INTENSITY       0.57      0.48      0.52       345

          accuracy                           0.48      1545
         macro avg       0.48      0.48      0.47      1545
      weighted avg       0.48      0.48      0.47      1545


Accuracy capturado en la ejecución 18: 48.09 [%]
F1-score capturado en la ejecución 18: 46.72 [%]

=== EJECUCIÓN 19 ===

--- TRAIN (ejecución 19) ---

--- TEST (ejecución 19) ---
2025-11-07 17:01:41.120063: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 17:01:41.131267: 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 00:00:1762531301.144316 3330274 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762531301.148437 3330274 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762531301.158219 3330274 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762531301.158237 3330274 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762531301.158246 3330274 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762531301.158247 3330274 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 17:01:41.161564: 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.
I0000 00:00:1762531303.441264 3330274 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13760 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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Epoch 1/28
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762531305.948696 3330405 service.cc:152] XLA service 0x7d1530015e50 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762531305.948744 3330405 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-07 17:01:46.007115: 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 00:00:1762531306.320977 3330405 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762531308.628702 3330405 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m24:49[0m 5s/step - accuracy: 0.2500 - loss: 1.9514
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Epoch 2/28

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Epoch 3/28

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Epoch 4/28

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[1m 29/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4047 - loss: 1.4682 
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Epoch 5/28

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[1m 32/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4194 - loss: 1.3973 
[1m 62/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4197 - loss: 1.3925
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[1m299/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4201 - loss: 1.3822
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Epoch 6/28

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[1m279/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4349 - loss: 1.3573
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Epoch 7/28

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[1m186/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4395 - loss: 1.3080
[1m217/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4390 - loss: 1.3101
[1m248/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4389 - loss: 1.3112
[1m279/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4388 - loss: 1.3122
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Epoch 8/28

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[1m 26/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4522 - loss: 1.3174 
[1m 57/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4504 - loss: 1.3128
[1m 89/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4527 - loss: 1.3090
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[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4509 - loss: 1.2965
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Epoch 9/28

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[1m 28/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4657 - loss: 1.3098 
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[1m 91/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4513 - loss: 1.3060
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[1m275/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4521 - loss: 1.2861
[1m307/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4527 - loss: 1.2844
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Epoch 10/28

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[1m268/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4574 - loss: 1.2655
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Epoch 11/28

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[1m 31/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5120 - loss: 1.1626 
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[1m185/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4913 - loss: 1.1981
[1m214/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4895 - loss: 1.2018
[1m244/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4878 - loss: 1.2053
[1m271/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4865 - loss: 1.2079
[1m301/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4852 - loss: 1.2107
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4841 - loss: 1.2130
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4840 - loss: 1.2131 - val_accuracy: 0.5270 - val_loss: 1.2155
Epoch 12/28

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[1m 32/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4775 - loss: 1.2259 
[1m 62/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4800 - loss: 1.2180
[1m 92/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4771 - loss: 1.2182
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[1m278/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4698 - loss: 1.2226
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Epoch 13/28

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[1m 31/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4434 - loss: 1.2624 
[1m 62/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4492 - loss: 1.2531
[1m 93/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4557 - loss: 1.2453
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[1m248/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4673 - loss: 1.2279
[1m276/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4679 - loss: 1.2274
[1m304/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4682 - loss: 1.2272
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Epoch 14/28

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[1m 27/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4803 - loss: 1.2044 
[1m 58/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4861 - loss: 1.1872
[1m 90/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4852 - loss: 1.1898
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[1m148/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4831 - loss: 1.1940
[1m180/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4828 - loss: 1.1943
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[1m239/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4823 - loss: 1.1955
[1m267/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4824 - loss: 1.1957
[1m299/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4827 - loss: 1.1958
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Epoch 15/28

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[1m122/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4887 - loss: 1.1959
[1m152/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4870 - loss: 1.1955
[1m181/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4855 - loss: 1.1950
[1m211/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4852 - loss: 1.1931
[1m241/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4850 - loss: 1.1918
[1m273/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4850 - loss: 1.1910
[1m301/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4848 - loss: 1.1905
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4848 - loss: 1.1899 - val_accuracy: 0.5330 - val_loss: 1.1837
Epoch 16/28

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[1m 29/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4780 - loss: 1.2082 
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[1m 90/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4851 - loss: 1.1916
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[1m183/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4851 - loss: 1.1874
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[1m269/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4850 - loss: 1.1858
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Epoch 17/28

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[1m299/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4763 - loss: 1.1811
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Epoch 18/28

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[1m274/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4949 - loss: 1.1652
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Epoch 19/28

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[1m182/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4958 - loss: 1.1568
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[1m305/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4985 - loss: 1.1522
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4988 - loss: 1.1515 - val_accuracy: 0.5358 - val_loss: 1.1785
Epoch 20/28

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[1m 30/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5259 - loss: 1.0725 
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Epoch 21/28

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[1m 32/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4694 - loss: 1.1696 
[1m 62/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4822 - loss: 1.1534
[1m 96/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4866 - loss: 1.1507
[1m126/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4892 - loss: 1.1475
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[1m242/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4928 - loss: 1.1432
[1m274/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4939 - loss: 1.1420
[1m303/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4948 - loss: 1.1412
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Epoch 22/28

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[1m 29/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4886 - loss: 1.1373 
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[1m182/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5095 - loss: 1.1237
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[1m244/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5100 - loss: 1.1245
[1m276/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5104 - loss: 1.1246
[1m306/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5107 - loss: 1.1248
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Epoch 23/28

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[1m 31/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4823 - loss: 1.1598 
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[1m 92/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4932 - loss: 1.1431
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[1m152/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5000 - loss: 1.1318
[1m183/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5013 - loss: 1.1295
[1m213/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5019 - loss: 1.1288
[1m243/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5024 - loss: 1.1283
[1m274/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5030 - loss: 1.1278
[1m305/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5034 - loss: 1.1276
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5039 - loss: 1.1275 - val_accuracy: 0.5404 - val_loss: 1.1613
Epoch 24/28

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[1m 30/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4883 - loss: 1.1848 
[1m 57/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4883 - loss: 1.1698
[1m 85/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4899 - loss: 1.1615
[1m117/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4914 - loss: 1.1561
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[1m178/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4936 - loss: 1.1511
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[1m239/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4967 - loss: 1.1458
[1m271/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4977 - loss: 1.1440
[1m302/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4984 - loss: 1.1426
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Epoch 25/28

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[1m 27/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5402 - loss: 1.1248 
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Epoch 26/28

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Epoch 27/28

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[1m295/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5197 - loss: 1.1267
[1m326/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.5198 - loss: 1.1250
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Epoch 28/28

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[1m119/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5307 - loss: 1.1064
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[1m269/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5282 - loss: 1.1011
[1m299/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5281 - loss: 1.1004
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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m23s[0m 493ms/step2025-11-07 17:02:13.592612: 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_141', 8 bytes spill stores, 8 bytes spill loads

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Saved model to disk.
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Loaded model from disk.
(1545, 3, 250)
(10469, 3, 250)

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7:11[0m 1s/step
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[1m53/89[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 970us/step
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step  
Global accuracy score (validation) = 50.07 [%]
Global F1 score (validation) = 48.61 [%]
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[[0.281218   0.34403506 0.22932716 0.14541984]
 [0.26321813 0.13163121 0.503802   0.10134859]
 [0.26927653 0.10647327 0.52157456 0.10267562]
 ...
 [0.2560748  0.17551902 0.4783093  0.09009684]
 [0.25100064 0.1603297  0.48578143 0.10288817]
 [0.2558337  0.23443311 0.42553166 0.08420146]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 57.98 [%]
Global accuracy score (test) = 50.61 [%]
Global F1 score (train) = 57.06 [%]
Global F1 score (test) = 49.28 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.39      0.24      0.30       400
MODERATE-INTENSITY       0.49      0.60      0.54       400
         SEDENTARY       0.52      0.70      0.60       400
VIGOROUS-INTENSITY       0.61      0.48      0.54       345

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


Accuracy capturado en la ejecución 19: 50.61 [%]
F1-score capturado en la ejecución 19: 49.28 [%]

=== EJECUCIÓN 20 ===

--- TRAIN (ejecución 20) ---

--- TEST (ejecución 20) ---
2025-11-07 17:02:24.487315: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 17:02:24.498496: 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 00:00:1762531344.511469 3333957 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762531344.515580 3333957 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762531344.525347 3333957 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762531344.525365 3333957 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762531344.525367 3333957 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762531344.525369 3333957 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 17:02:24.528504: 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.
I0000 00:00:1762531346.773217 3333957 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13758 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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Epoch 1/28
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762531349.236155 3334067 service.cc:152] XLA service 0x7590bc003d60 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762531349.236189 3334067 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-07 17:02:29.286447: 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 00:00:1762531349.601389 3334067 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762531351.913423 3334067 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m24:29[0m 4s/step - accuracy: 0.2188 - loss: 1.9886
[1m 26/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2400 - loss: 1.9418  
[1m 58/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2433 - loss: 1.9312
[1m 87/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2497 - loss: 1.9057
[1m117/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2567 - loss: 1.8807
[1m148/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2630 - loss: 1.8611
[1m176/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2679 - loss: 1.8462
[1m205/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2723 - loss: 1.8320
[1m238/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2765 - loss: 1.8175
[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2804 - loss: 1.8045
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Epoch 2/28

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Epoch 3/28

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Epoch 4/28

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Epoch 5/28

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Epoch 6/28

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[1m271/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4518 - loss: 1.3150
[1m299/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4516 - loss: 1.3162
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Epoch 7/28

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[1m299/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4448 - loss: 1.3113
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4452 - loss: 1.3102
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4452 - loss: 1.3101 - val_accuracy: 0.4874 - val_loss: 1.1983
Epoch 8/28

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.3438 - loss: 1.4365
[1m 32/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4427 - loss: 1.2862 
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[1m124/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4528 - loss: 1.2882
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[1m189/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4531 - loss: 1.2863
[1m220/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4532 - loss: 1.2858
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[1m281/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4535 - loss: 1.2848
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Epoch 9/28

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4375 - loss: 1.2217
[1m 28/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4699 - loss: 1.2559 
[1m 58/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4688 - loss: 1.2650
[1m 89/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4684 - loss: 1.2582
[1m119/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4678 - loss: 1.2548
[1m149/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4689 - loss: 1.2514
[1m182/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4694 - loss: 1.2504
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[1m245/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4693 - loss: 1.2510
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Epoch 10/28

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[1m 31/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4761 - loss: 1.2306 
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Epoch 11/28

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[1m292/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4709 - loss: 1.2409
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Epoch 12/28

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[1m 28/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5294 - loss: 1.1770 
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[1m119/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4929 - loss: 1.2182
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Epoch 13/28

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[1m 28/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4789 - loss: 1.1701 
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Epoch 14/28

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Epoch 15/28

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Epoch 16/28

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[1m 30/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5070 - loss: 1.1453 
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[1m121/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5020 - loss: 1.1631
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[1m180/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4994 - loss: 1.1679
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[1m273/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4963 - loss: 1.1730
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Epoch 17/28

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[1m305/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4907 - loss: 1.1696
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Epoch 18/28

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[1m 27/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4596 - loss: 1.2018 
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[1m268/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4985 - loss: 1.1546
[1m300/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4988 - loss: 1.1542
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Epoch 19/28

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[1m120/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5047 - loss: 1.1439
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[1m182/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5017 - loss: 1.1466
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[1m242/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5012 - loss: 1.1470
[1m271/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5010 - loss: 1.1471
[1m300/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5010 - loss: 1.1468
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5011 - loss: 1.1465 - val_accuracy: 0.5081 - val_loss: 1.1562
Epoch 20/28

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[1m 32/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5040 - loss: 1.1240 
[1m 65/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5025 - loss: 1.1332
[1m 98/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5017 - loss: 1.1349
[1m128/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5018 - loss: 1.1347
[1m159/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5015 - loss: 1.1357
[1m191/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5011 - loss: 1.1372
[1m222/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5007 - loss: 1.1384
[1m251/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5006 - loss: 1.1394
[1m280/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5007 - loss: 1.1401
[1m311/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5008 - loss: 1.1406
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5009 - loss: 1.1409 - val_accuracy: 0.4923 - val_loss: 1.1563
Epoch 21/28

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.5312 - loss: 1.1017
[1m 29/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5058 - loss: 1.1531 
[1m 60/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5023 - loss: 1.1501
[1m 91/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5019 - loss: 1.1507
[1m121/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5051 - loss: 1.1460
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[1m179/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5080 - loss: 1.1415
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[1m241/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5093 - loss: 1.1385
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Epoch 22/28

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[1m 30/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5112 - loss: 1.1190 
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[1m 88/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5101 - loss: 1.1233
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[1m181/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5118 - loss: 1.1255
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[1m243/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5123 - loss: 1.1258
[1m274/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5123 - loss: 1.1253
[1m304/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5120 - loss: 1.1252
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Epoch 23/28

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[1m 30/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5431 - loss: 1.0906 
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[1m120/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5287 - loss: 1.0949
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[1m182/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5246 - loss: 1.0973
[1m211/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5232 - loss: 1.0992
[1m243/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5221 - loss: 1.1015
[1m271/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5214 - loss: 1.1032
[1m301/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5209 - loss: 1.1046
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5204 - loss: 1.1055 - val_accuracy: 0.5098 - val_loss: 1.1418
Epoch 24/28

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.6562 - loss: 0.8831
[1m 29/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5337 - loss: 1.0738 
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[1m 89/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5202 - loss: 1.0859
[1m120/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5177 - loss: 1.0902
[1m150/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5173 - loss: 1.0932
[1m182/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5174 - loss: 1.0962
[1m211/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5174 - loss: 1.0979
[1m240/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5174 - loss: 1.0993
[1m269/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5172 - loss: 1.1006
[1m301/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5171 - loss: 1.1017
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5168 - loss: 1.1028 - val_accuracy: 0.5067 - val_loss: 1.1414
Epoch 25/28

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5312 - loss: 1.1449
[1m 32/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4961 - loss: 1.1221 
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[1m 86/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5058 - loss: 1.1049
[1m114/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5091 - loss: 1.1019
[1m142/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5117 - loss: 1.1002
[1m173/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5136 - loss: 1.1002
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Epoch 26/28

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Epoch 27/28

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Epoch 28/28

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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m22s[0m 473ms/step2025-11-07 17:02:56.976189: 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_141', 8 bytes spill stores, 8 bytes spill loads

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Saved model to disk.
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
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Loaded model from disk.
(1545, 3, 250)
(10469, 3, 250)

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7:07[0m 1s/step
[1m 51/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step 
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[1m167/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 909us/step
[1m231/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 876us/step
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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 16ms/step
[1m47/49[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step 
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 19ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 19ms/step

[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 15ms/step
[1m59/89[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 872us/step
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step  
Global accuracy score (validation) = 52.81 [%]
Global F1 score (validation) = 47.9 [%]
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[[0.3000908  0.25239962 0.43791085 0.00959871]
 [0.16155823 0.13040899 0.56226635 0.14576647]
 [0.01535998 0.02067281 0.9446451  0.01932219]
 ...
 [0.22447221 0.14233804 0.5574889  0.07570084]
 [0.23720303 0.16634429 0.49496603 0.10148665]
 [0.19950122 0.17866574 0.5480037  0.07382933]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 56.31 [%]
Global accuracy score (test) = 52.04 [%]
Global F1 score (train) = 52.97 [%]
Global F1 score (test) = 47.31 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.56      0.09      0.15       400
MODERATE-INTENSITY       0.47      0.76      0.58       400
         SEDENTARY       0.49      0.79      0.60       400
VIGOROUS-INTENSITY       0.76      0.44      0.55       345

          accuracy                           0.52      1545
         macro avg       0.57      0.52      0.47      1545
      weighted avg       0.56      0.52      0.47      1545


Accuracy capturado en la ejecución 20: 52.04 [%]
F1-score capturado en la ejecución 20: 47.31 [%]

=== EJECUCIÓN 21 ===

--- TRAIN (ejecución 21) ---

--- TEST (ejecución 21) ---
2025-11-07 17:03:07.974235: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 17:03:07.985707: 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 00:00:1762531387.998963 3337608 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762531388.003135 3337608 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762531388.013698 3337608 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762531388.013720 3337608 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762531388.013722 3337608 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762531388.013724 3337608 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 17:03:08.016879: 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.
I0000 00:00:1762531390.289207 3337608 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13758 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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Epoch 1/28
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762531392.742971 3337740 service.cc:152] XLA service 0x7594440273e0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762531392.743009 3337740 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-07 17:03:12.793946: 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 00:00:1762531393.106213 3337740 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762531395.402530 3337740 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m24:25[0m 4s/step - accuracy: 0.3438 - loss: 1.5814
[1m 28/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2644 - loss: 1.8843  
[1m 59/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2607 - loss: 1.8878
[1m 92/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2612 - loss: 1.8825
[1m122/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2634 - loss: 1.8750
[1m151/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2653 - loss: 1.8705
[1m183/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2679 - loss: 1.8621
[1m214/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2706 - loss: 1.8533
[1m243/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2731 - loss: 1.8455
[1m273/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2757 - loss: 1.8377
[1m305/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.2786 - loss: 1.8297
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Epoch 2/28

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[1m 28/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3378 - loss: 1.6116 
[1m 58/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3504 - loss: 1.6017
[1m 91/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3588 - loss: 1.5927
[1m121/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3620 - loss: 1.5892
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Epoch 3/28

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[1m280/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3996 - loss: 1.4589
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Epoch 4/28

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[1m184/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4010 - loss: 1.4446
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[1m246/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4028 - loss: 1.4414
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[1m308/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4047 - loss: 1.4371
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Epoch 5/28

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[1m 32/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4154 - loss: 1.3266 
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[1m 95/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4094 - loss: 1.3768
[1m123/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4101 - loss: 1.3804
[1m152/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4108 - loss: 1.3827
[1m183/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4119 - loss: 1.3833
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Epoch 6/28

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[1m 32/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4244 - loss: 1.3657 
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[1m 85/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4226 - loss: 1.3540
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Epoch 7/28

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[1m279/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4390 - loss: 1.3108
[1m310/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4393 - loss: 1.3102
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Epoch 8/28

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[1m191/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4410 - loss: 1.2926
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[1m254/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4417 - loss: 1.2904
[1m287/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4424 - loss: 1.2887
[1m321/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.4431 - loss: 1.2875
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4433 - loss: 1.2872 - val_accuracy: 0.4853 - val_loss: 1.1745
Epoch 9/28

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[1m 31/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4652 - loss: 1.2269 
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[1m 92/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4636 - loss: 1.2369
[1m122/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4619 - loss: 1.2415
[1m152/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4615 - loss: 1.2438
[1m182/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4608 - loss: 1.2456
[1m212/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4600 - loss: 1.2475
[1m243/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4597 - loss: 1.2487
[1m272/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4594 - loss: 1.2496
[1m302/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4591 - loss: 1.2502
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4589 - loss: 1.2505 - val_accuracy: 0.4828 - val_loss: 1.1703
Epoch 10/28

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[1m 30/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4774 - loss: 1.2284 
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[1m121/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4722 - loss: 1.2290
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[1m296/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4625 - loss: 1.2394
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Epoch 11/28

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[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4731 - loss: 1.2195
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Epoch 12/28

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[1m 28/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4515 - loss: 1.2379 
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[1m120/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4670 - loss: 1.2293
[1m150/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4680 - loss: 1.2280
[1m180/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4690 - loss: 1.2269
[1m209/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4699 - loss: 1.2255
[1m236/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4705 - loss: 1.2241
[1m263/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4708 - loss: 1.2232
[1m293/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4708 - loss: 1.2229
[1m324/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.4709 - loss: 1.2226
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4709 - loss: 1.2225 - val_accuracy: 0.4996 - val_loss: 1.1543
Epoch 13/28

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 21ms/step - accuracy: 0.3750 - loss: 1.2829
[1m 32/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4719 - loss: 1.1807 
[1m 59/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4734 - loss: 1.1935
[1m 89/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4757 - loss: 1.1988
[1m121/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4777 - loss: 1.2000
[1m150/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4784 - loss: 1.2010
[1m180/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4786 - loss: 1.2020
[1m212/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4784 - loss: 1.2028
[1m245/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4782 - loss: 1.2033
[1m276/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4779 - loss: 1.2037
[1m306/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4778 - loss: 1.2041
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Epoch 14/28

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.6562 - loss: 0.9947
[1m 27/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4959 - loss: 1.1813 
[1m 60/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4870 - loss: 1.1948
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[1m187/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4826 - loss: 1.1964
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Epoch 15/28

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[1m273/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4876 - loss: 1.1830
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Epoch 16/28

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[1m182/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4906 - loss: 1.1663
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[1m244/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4908 - loss: 1.1684
[1m276/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4907 - loss: 1.1689
[1m306/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4909 - loss: 1.1689
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4911 - loss: 1.1688 - val_accuracy: 0.4902 - val_loss: 1.1402
Epoch 17/28

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[1m 31/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4679 - loss: 1.1538 
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[1m 94/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4829 - loss: 1.1617
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[1m185/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4866 - loss: 1.1651
[1m218/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4872 - loss: 1.1652
[1m245/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4875 - loss: 1.1653
[1m278/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4880 - loss: 1.1652
[1m306/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4884 - loss: 1.1649
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Epoch 18/28

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[1m 28/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4699 - loss: 1.1890 
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[1m181/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4803 - loss: 1.1704
[1m210/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4819 - loss: 1.1680
[1m242/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4835 - loss: 1.1659
[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4846 - loss: 1.1642
[1m297/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4855 - loss: 1.1630
[1m326/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.4861 - loss: 1.1621
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Epoch 19/28

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 21ms/step - accuracy: 0.5938 - loss: 0.9214
[1m 31/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5181 - loss: 1.0747 
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[1m188/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5091 - loss: 1.1141
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[1m252/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5072 - loss: 1.1192
[1m282/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5064 - loss: 1.1213
[1m314/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.5055 - loss: 1.1232
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5051 - loss: 1.1241 - val_accuracy: 0.5084 - val_loss: 1.1303
Epoch 20/28

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[1m 33/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4704 - loss: 1.1808 
[1m 65/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4776 - loss: 1.1659
[1m 97/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4836 - loss: 1.1585
[1m130/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4864 - loss: 1.1564
[1m161/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4878 - loss: 1.1562
[1m193/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4894 - loss: 1.1548
[1m224/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4904 - loss: 1.1536
[1m254/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4911 - loss: 1.1526
[1m288/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4915 - loss: 1.1516
[1m318/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.4919 - loss: 1.1506
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4920 - loss: 1.1503 - val_accuracy: 0.5021 - val_loss: 1.1298
Epoch 21/28

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5000 - loss: 1.0722
[1m 30/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5112 - loss: 1.1215 
[1m 60/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5101 - loss: 1.1185
[1m 91/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5099 - loss: 1.1161
[1m122/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5108 - loss: 1.1139
[1m152/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5117 - loss: 1.1128
[1m183/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5119 - loss: 1.1134
[1m214/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5118 - loss: 1.1142
[1m241/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5113 - loss: 1.1154
[1m273/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5107 - loss: 1.1164
[1m304/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5102 - loss: 1.1173
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5099 - loss: 1.1179 - val_accuracy: 0.5172 - val_loss: 1.1225
Epoch 22/28

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4062 - loss: 1.1142
[1m 32/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4800 - loss: 1.1657 
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[1m 90/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4931 - loss: 1.1503
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[1m180/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4994 - loss: 1.1424
[1m213/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5007 - loss: 1.1406
[1m244/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5015 - loss: 1.1397
[1m274/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5020 - loss: 1.1388
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Epoch 23/28

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5625 - loss: 0.9932
[1m 32/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4872 - loss: 1.1435 
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[1m183/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5014 - loss: 1.1302
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[1m244/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5035 - loss: 1.1263
[1m275/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5042 - loss: 1.1253
[1m306/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5046 - loss: 1.1247
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Epoch 24/28

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5000 - loss: 1.1943
[1m 33/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4918 - loss: 1.1072 
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[1m128/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5034 - loss: 1.1057
[1m160/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5054 - loss: 1.1056
[1m191/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5065 - loss: 1.1059
[1m223/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5078 - loss: 1.1057
[1m250/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5088 - loss: 1.1059
[1m281/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5097 - loss: 1.1061
[1m309/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5103 - loss: 1.1063
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5106 - loss: 1.1064 - val_accuracy: 0.5088 - val_loss: 1.1251
Epoch 25/28

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4688 - loss: 1.2894
[1m 31/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5202 - loss: 1.1136 
[1m 61/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5169 - loss: 1.1205
[1m 91/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5143 - loss: 1.1193
[1m122/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5127 - loss: 1.1167
[1m152/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5125 - loss: 1.1153
[1m183/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5130 - loss: 1.1144
[1m215/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5135 - loss: 1.1139
[1m245/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5138 - loss: 1.1135
[1m274/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5141 - loss: 1.1135
[1m303/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5144 - loss: 1.1133
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5145 - loss: 1.1132 - val_accuracy: 0.5067 - val_loss: 1.1256
Epoch 26/28

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5000 - loss: 1.3021
[1m 30/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5364 - loss: 1.0895 
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Epoch 27/28

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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m24s[0m 502ms/step2025-11-07 17:03:39.408616: 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_141', 8 bytes spill stores, 8 bytes spill loads

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Saved model to disk.
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Loaded model from disk.
(1545, 3, 250)
(10469, 3, 250)

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[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 15ms/step
[1m55/89[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 928us/step
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Global accuracy score (validation) = 51.4 [%]
Global F1 score (validation) = 50.01 [%]
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[[0.45067033 0.140038   0.39654183 0.01274981]
 [0.12656562 0.25845933 0.5541718  0.06080327]
 [0.03957871 0.01651141 0.91114265 0.03276726]
 ...
 [0.34217173 0.10344331 0.5016148  0.05277018]
 [0.34006137 0.11402489 0.41274375 0.13317002]
 [0.37516433 0.13418545 0.45222622 0.03842409]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 56.51 [%]
Global accuracy score (test) = 48.48 [%]
Global F1 score (train) = 55.84 [%]
Global F1 score (test) = 48.07 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.38      0.44      0.41       400
MODERATE-INTENSITY       0.51      0.30      0.38       400
         SEDENTARY       0.49      0.70      0.58       400
VIGOROUS-INTENSITY       0.64      0.50      0.56       345

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


Accuracy capturado en la ejecución 21: 48.48 [%]
F1-score capturado en la ejecución 21: 48.07 [%]

=== EJECUCIÓN 22 ===

--- TRAIN (ejecución 22) ---

--- TEST (ejecución 22) ---
2025-11-07 17:03:50.306345: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 17:03:50.317847: 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 00:00:1762531430.330844 3341185 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762531430.335050 3341185 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762531430.344921 3341185 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762531430.344937 3341185 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762531430.344939 3341185 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762531430.344941 3341185 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 17:03:50.348057: 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.
I0000 00:00:1762531432.554954 3341185 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13758 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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Epoch 1/28
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762531435.049618 3341296 service.cc:152] XLA service 0x74d0bc0147c0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762531435.049678 3341296 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-07 17:03:55.106244: 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 00:00:1762531435.421233 3341296 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762531437.756008 3341296 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

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[1m 88/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2523 - loss: 1.9199
[1m120/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2567 - loss: 1.9048
[1m153/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2611 - loss: 1.8887
[1m187/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2655 - loss: 1.8741
[1m218/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2692 - loss: 1.8623
[1m249/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2727 - loss: 1.8510
[1m280/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2759 - loss: 1.8404
[1m312/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.2790 - loss: 1.8300
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step - accuracy: 0.2805 - loss: 1.8250
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m9s[0m 13ms/step - accuracy: 0.2806 - loss: 1.8247 - val_accuracy: 0.3862 - val_loss: 1.2690
Epoch 2/28

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[1m 31/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3890 - loss: 1.5050 
[1m 61/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3737 - loss: 1.5238
[1m 92/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3690 - loss: 1.5311
[1m122/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3667 - loss: 1.5328
[1m156/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3653 - loss: 1.5348
[1m188/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3648 - loss: 1.5364
[1m217/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3646 - loss: 1.5378
[1m249/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3646 - loss: 1.5391
[1m280/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3644 - loss: 1.5394
[1m312/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.3641 - loss: 1.5395
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3641 - loss: 1.5393 - val_accuracy: 0.4673 - val_loss: 1.2130
Epoch 3/28

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.3438 - loss: 1.6592
[1m 30/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4134 - loss: 1.4466 
[1m 62/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3992 - loss: 1.4591
[1m 89/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3968 - loss: 1.4584
[1m119/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3952 - loss: 1.4561
[1m151/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3949 - loss: 1.4519
[1m183/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3947 - loss: 1.4493
[1m213/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3949 - loss: 1.4473
[1m241/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3950 - loss: 1.4460
[1m274/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3954 - loss: 1.4445
[1m303/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.3957 - loss: 1.4434
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3961 - loss: 1.4425 - val_accuracy: 0.4702 - val_loss: 1.1927
Epoch 4/28

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[1m 29/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4179 - loss: 1.4114 
[1m 60/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4251 - loss: 1.3967
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Epoch 5/28

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Epoch 6/28

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Epoch 7/28

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[1m126/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4624 - loss: 1.2804
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[1m187/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4576 - loss: 1.2835
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Epoch 8/28

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[1m 29/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4153 - loss: 1.3906 
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[1m218/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4411 - loss: 1.3122
[1m250/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4425 - loss: 1.3073
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Epoch 9/28

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[1m181/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4554 - loss: 1.2529
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[1m239/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4558 - loss: 1.2528
[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4559 - loss: 1.2525
[1m301/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4562 - loss: 1.2523
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Epoch 10/28

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[1m119/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4810 - loss: 1.2082
[1m151/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4789 - loss: 1.2114
[1m180/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4780 - loss: 1.2137
[1m213/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4771 - loss: 1.2154
[1m243/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4762 - loss: 1.2171
[1m275/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4753 - loss: 1.2188
[1m306/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4747 - loss: 1.2200
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4745 - loss: 1.2205 - val_accuracy: 0.5067 - val_loss: 1.1465
Epoch 11/28

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5000 - loss: 1.1906
[1m 33/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4540 - loss: 1.2543 
[1m 65/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4593 - loss: 1.2483
[1m 97/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4616 - loss: 1.2465
[1m124/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4625 - loss: 1.2454
[1m156/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4638 - loss: 1.2439
[1m184/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4641 - loss: 1.2431
[1m215/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4638 - loss: 1.2432
[1m241/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4637 - loss: 1.2431
[1m271/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4637 - loss: 1.2428
[1m301/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4639 - loss: 1.2419
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4639 - loss: 1.2413 - val_accuracy: 0.5133 - val_loss: 1.1342
Epoch 12/28

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4375 - loss: 1.3155
[1m 32/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4512 - loss: 1.2691 
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[1m 93/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4541 - loss: 1.2624
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Epoch 13/28

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[1m268/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4663 - loss: 1.2123
[1m298/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4666 - loss: 1.2114
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Epoch 14/28

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[1m125/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4751 - loss: 1.1888
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[1m187/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4759 - loss: 1.1891
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[1m279/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4776 - loss: 1.1878
[1m309/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4779 - loss: 1.1878
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4781 - loss: 1.1878 - val_accuracy: 0.5077 - val_loss: 1.1357
Epoch 15/28

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.5312 - loss: 1.1010
[1m 30/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4947 - loss: 1.1798 
[1m 58/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4987 - loss: 1.1741
[1m 89/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4970 - loss: 1.1736
[1m119/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4942 - loss: 1.1744
[1m150/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4923 - loss: 1.1760
[1m176/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4907 - loss: 1.1775
[1m208/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4894 - loss: 1.1780
[1m240/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4890 - loss: 1.1785
[1m272/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4886 - loss: 1.1789
[1m301/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4880 - loss: 1.1795
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Epoch 16/28

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4688 - loss: 1.1952
[1m 32/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5038 - loss: 1.1446 
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[1m 95/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4915 - loss: 1.1642
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Epoch 17/28

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[1m279/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4937 - loss: 1.1590
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Epoch 18/28

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[1m157/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4820 - loss: 1.1738
[1m187/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4831 - loss: 1.1716
[1m218/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4839 - loss: 1.1701
[1m249/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4844 - loss: 1.1693
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[1m312/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.4852 - loss: 1.1681
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4854 - loss: 1.1678 - val_accuracy: 0.5095 - val_loss: 1.1317
Epoch 19/28

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[1m 30/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4691 - loss: 1.1369 
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[1m 92/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4776 - loss: 1.1427
[1m125/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4803 - loss: 1.1419
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[1m186/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4827 - loss: 1.1441
[1m214/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4838 - loss: 1.1452
[1m246/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4849 - loss: 1.1459
[1m274/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4859 - loss: 1.1459
[1m306/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4870 - loss: 1.1459
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Epoch 20/28

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[1m 32/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4922 - loss: 1.1571 
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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m24s[0m 511ms/step
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Saved model to disk.
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Loaded model from disk.
(1545, 3, 250)
(10469, 3, 250)

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7:02[0m 1s/step
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[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m47/89[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step 
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Global accuracy score (validation) = 51.4 [%]
Global F1 score (validation) = 49.77 [%]
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[[0.25791663 0.13648793 0.48200208 0.12359336]
 [0.26507032 0.14351542 0.47082785 0.12058634]
 [0.26824695 0.15029523 0.46288595 0.11857189]
 ...
 [0.2841888  0.18121113 0.4563272  0.07827288]
 [0.2997989  0.16034561 0.43792877 0.10192668]
 [0.24969126 0.26754478 0.39962962 0.08313432]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 56.61 [%]
Global accuracy score (test) = 48.48 [%]
Global F1 score (train) = 56.08 [%]
Global F1 score (test) = 47.9 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.41      0.33      0.36       400
MODERATE-INTENSITY       0.54      0.43      0.48       400
         SEDENTARY       0.44      0.72      0.55       400
VIGOROUS-INTENSITY       0.61      0.46      0.52       345

          accuracy                           0.48      1545
         macro avg       0.50      0.48      0.48      1545
      weighted avg       0.50      0.48      0.48      1545


Accuracy capturado en la ejecución 22: 48.48 [%]
F1-score capturado en la ejecución 22: 47.9 [%]

=== EJECUCIÓN 23 ===

--- TRAIN (ejecución 23) ---

--- TEST (ejecución 23) ---
2025-11-07 17:04:27.657681: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 17:04:27.669369: 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 00:00:1762531467.682712 3344084 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762531467.686966 3344084 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762531467.697288 3344084 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762531467.697310 3344084 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762531467.697313 3344084 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762531467.697315 3344084 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 17:04:27.700365: 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.
I0000 00:00:1762531469.944905 3344084 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13758 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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Epoch 1/28
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762531472.427014 3344213 service.cc:152] XLA service 0x7e7bd8002ec0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762531472.427098 3344213 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-07 17:04:32.487869: 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 00:00:1762531472.804497 3344213 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762531475.141884 3344213 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m24:55[0m 5s/step - accuracy: 0.1875 - loss: 1.9642
[1m 29/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2266 - loss: 2.0761  
[1m 60/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2341 - loss: 2.0351
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Epoch 2/28

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Epoch 3/28

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[1m298/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.3949 - loss: 1.4837
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Epoch 4/28

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[1m 28/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4027 - loss: 1.4239 
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Epoch 5/28

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[1m 31/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4137 - loss: 1.3620 
[1m 61/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4088 - loss: 1.3685
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[1m266/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4144 - loss: 1.3707
[1m295/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4150 - loss: 1.3699
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Epoch 6/28

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[1m280/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4275 - loss: 1.3255
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Epoch 7/28

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[1m182/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4448 - loss: 1.3234
[1m213/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4457 - loss: 1.3203
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Epoch 8/28

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[1m 29/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4329 - loss: 1.3032 
[1m 60/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4427 - loss: 1.2969
[1m 91/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4445 - loss: 1.2987
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[1m273/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4481 - loss: 1.2971
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Epoch 9/28

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[1m 30/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4743 - loss: 1.2142 
[1m 60/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4765 - loss: 1.2196
[1m 89/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4742 - loss: 1.2249
[1m121/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4709 - loss: 1.2312
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[1m183/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4681 - loss: 1.2374
[1m217/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4676 - loss: 1.2387
[1m249/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4671 - loss: 1.2393
[1m279/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4666 - loss: 1.2400
[1m309/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4661 - loss: 1.2412
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Epoch 10/28

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[1m 29/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4623 - loss: 1.1977 
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[1m 90/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4558 - loss: 1.2365
[1m124/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4556 - loss: 1.2405
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[1m185/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4577 - loss: 1.2427
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[1m246/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4590 - loss: 1.2433
[1m276/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4597 - loss: 1.2430
[1m302/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4603 - loss: 1.2425
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4607 - loss: 1.2420 - val_accuracy: 0.4867 - val_loss: 1.1804
Epoch 11/28

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[1m 27/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4564 - loss: 1.2501 
[1m 56/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4585 - loss: 1.2510
[1m 87/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4552 - loss: 1.2560
[1m117/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4558 - loss: 1.2529
[1m148/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4567 - loss: 1.2489
[1m179/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4574 - loss: 1.2460
[1m211/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4584 - loss: 1.2436
[1m241/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4594 - loss: 1.2415
[1m273/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4601 - loss: 1.2398
[1m304/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4606 - loss: 1.2384
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4612 - loss: 1.2373 - val_accuracy: 0.4807 - val_loss: 1.1787
Epoch 12/28

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[1m 28/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4859 - loss: 1.2028 
[1m 60/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4865 - loss: 1.2064
[1m 93/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4859 - loss: 1.2043
[1m120/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4848 - loss: 1.2032
[1m152/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4830 - loss: 1.2025
[1m184/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4814 - loss: 1.2024
[1m216/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4799 - loss: 1.2026
[1m246/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4787 - loss: 1.2035
[1m277/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4777 - loss: 1.2048
[1m309/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4769 - loss: 1.2059
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Epoch 13/28

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[1m 32/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5034 - loss: 1.2179 
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Epoch 14/28

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[1m274/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4803 - loss: 1.1903
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Epoch 15/28

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[1m186/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4814 - loss: 1.1864
[1m217/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4812 - loss: 1.1882
[1m245/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4807 - loss: 1.1897
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[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4797 - loss: 1.1922 - val_accuracy: 0.4838 - val_loss: 1.1636
Epoch 16/28

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[1m 31/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5021 - loss: 1.1890 
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[1m125/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4974 - loss: 1.1818
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[1m180/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4946 - loss: 1.1817
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Epoch 17/28

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[1m 28/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4795 - loss: 1.1928 
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[1m243/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4922 - loss: 1.1607
[1m273/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4922 - loss: 1.1612
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Epoch 18/28

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[1m272/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4899 - loss: 1.1699
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Epoch 19/28

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[1m125/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5143 - loss: 1.1299
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[1m180/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5125 - loss: 1.1324
[1m210/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5119 - loss: 1.1335
[1m238/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5111 - loss: 1.1347
[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5103 - loss: 1.1361
[1m302/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5094 - loss: 1.1375
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5085 - loss: 1.1387 - val_accuracy: 0.4954 - val_loss: 1.1550
Epoch 20/28

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[1m 29/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4803 - loss: 1.1669 
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[1m 86/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4952 - loss: 1.1512
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[1m147/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5028 - loss: 1.1433
[1m178/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5050 - loss: 1.1410
[1m209/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5059 - loss: 1.1403
[1m240/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5063 - loss: 1.1404
[1m272/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5068 - loss: 1.1401
[1m304/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5069 - loss: 1.1401
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Epoch 21/28

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[1m 31/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5060 - loss: 1.1366 
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Epoch 22/28

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Epoch 23/28

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[1m180/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5203 - loss: 1.1040
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Epoch 24/28

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5000 - loss: 1.1372
[1m 32/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5039 - loss: 1.1318 
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[1m 92/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5051 - loss: 1.1320
[1m125/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5083 - loss: 1.1297
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Epoch 25/28

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4062 - loss: 1.1376
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Epoch 26/28

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[1m 57/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5499 - loss: 1.0681
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[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5220 - loss: 1.1004 - val_accuracy: 0.5056 - val_loss: 1.1361
Epoch 27/28

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5625 - loss: 1.0601
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[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5134 - loss: 1.1097 - val_accuracy: 0.5081 - val_loss: 1.1336

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m23s[0m 483ms/step2025-11-07 17:04:59.374120: 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_141', 8 bytes spill stores, 8 bytes spill loads


[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 21ms/step  
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 21ms/step
Saved model to disk.
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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Loaded model from disk.
(1545, 3, 250)
(10469, 3, 250)

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[1m175/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 871us/step
[1m238/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 852us/step
[1m305/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 831us/step
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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 16ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 18ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 19ms/step

[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m53/89[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 972us/step
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step  
Global accuracy score (validation) = 49.4 [%]
Global F1 score (validation) = 49.2 [%]
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[[0.20716718 0.07819042 0.5932511  0.12139127]
 [0.51477724 0.19362688 0.27759442 0.01400139]
 [0.51851076 0.19264042 0.2748335  0.01401533]
 ...
 [0.3364483  0.12838534 0.47411957 0.06104676]
 [0.41968927 0.11491504 0.3674036  0.09799202]
 [0.309832   0.17558576 0.46909064 0.04549162]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 54.56 [%]
Global accuracy score (test) = 48.03 [%]
Global F1 score (train) = 54.53 [%]
Global F1 score (test) = 48.11 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.36      0.44      0.40       400
MODERATE-INTENSITY       0.51      0.36      0.42       400
         SEDENTARY       0.49      0.67      0.57       400
VIGOROUS-INTENSITY       0.69      0.44      0.54       345

          accuracy                           0.48      1545
         macro avg       0.51      0.48      0.48      1545
      weighted avg       0.51      0.48      0.48      1545


Accuracy capturado en la ejecución 23: 48.03 [%]
F1-score capturado en la ejecución 23: 48.11 [%]

=== EJECUCIÓN 24 ===

--- TRAIN (ejecución 24) ---

--- TEST (ejecución 24) ---
2025-11-07 17:05:10.285389: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 17:05:10.297326: 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 00:00:1762531510.311398 3347662 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762531510.315777 3347662 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762531510.325977 3347662 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762531510.325995 3347662 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762531510.325997 3347662 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762531510.325999 3347662 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 17:05:10.329241: 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.
I0000 00:00:1762531512.575629 3347662 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13758 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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Epoch 1/28
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762531515.035728 3347793 service.cc:152] XLA service 0x7bcf54003050 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762531515.035759 3347793 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-07 17:05:15.085584: 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 00:00:1762531515.404884 3347793 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762531517.735187 3347793 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m24:35[0m 5s/step - accuracy: 0.3438 - loss: 1.6023
[1m 29/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2483 - loss: 1.9427  
[1m 57/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2567 - loss: 1.9218
[1m 86/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2620 - loss: 1.9100
[1m118/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2692 - loss: 1.8886
[1m150/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2758 - loss: 1.8705
[1m178/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2805 - loss: 1.8567
[1m211/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2856 - loss: 1.8423
[1m238/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2890 - loss: 1.8323
[1m271/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2922 - loss: 1.8216
[1m302/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.2952 - loss: 1.8112
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step - accuracy: 0.2975 - loss: 1.8029
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m9s[0m 13ms/step - accuracy: 0.2976 - loss: 1.8026 - val_accuracy: 0.4572 - val_loss: 1.3676
Epoch 2/28

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[1m 31/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3560 - loss: 1.5719 
[1m 63/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3643 - loss: 1.5540
[1m 93/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3664 - loss: 1.5558
[1m125/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3691 - loss: 1.5536
[1m158/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3714 - loss: 1.5513
[1m188/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3730 - loss: 1.5484
[1m217/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3742 - loss: 1.5459
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[1m281/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3771 - loss: 1.5406
[1m311/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.3783 - loss: 1.5377
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Epoch 3/28

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Epoch 4/28

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Epoch 5/28

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Epoch 6/28

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[1m 28/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3941 - loss: 1.3268 
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Epoch 7/28

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[1m 31/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4087 - loss: 1.3392 
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[1m274/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4333 - loss: 1.3040
[1m306/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4343 - loss: 1.3032
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Epoch 8/28

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[1m 92/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4442 - loss: 1.2950
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Epoch 9/28

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[1m 28/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4789 - loss: 1.1766 
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Epoch 10/28

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[1m 30/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4880 - loss: 1.1970 
[1m 58/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4781 - loss: 1.2140
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Epoch 11/28

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[1m271/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4609 - loss: 1.2320
[1m304/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4613 - loss: 1.2319
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Epoch 12/28

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[1m 28/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4435 - loss: 1.2896 
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[1m119/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4578 - loss: 1.2492
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[1m181/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4594 - loss: 1.2433
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[1m273/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4601 - loss: 1.2393
[1m303/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4605 - loss: 1.2381
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4607 - loss: 1.2371 - val_accuracy: 0.5130 - val_loss: 1.2105
Epoch 13/28

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.3438 - loss: 1.6237
[1m 29/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4608 - loss: 1.2538 
[1m 59/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4604 - loss: 1.2370
[1m 91/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4626 - loss: 1.2279
[1m123/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4652 - loss: 1.2206
[1m155/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4671 - loss: 1.2147
[1m186/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4690 - loss: 1.2094
[1m218/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4698 - loss: 1.2064
[1m249/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4701 - loss: 1.2051
[1m279/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4702 - loss: 1.2047
[1m309/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4702 - loss: 1.2048
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4702 - loss: 1.2049 - val_accuracy: 0.5144 - val_loss: 1.2055
Epoch 14/28

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.5000 - loss: 1.2906
[1m 31/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4882 - loss: 1.1951 
[1m 61/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4845 - loss: 1.1946
[1m 93/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4832 - loss: 1.1935
[1m125/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4828 - loss: 1.1934
[1m159/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4829 - loss: 1.1930
[1m190/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4831 - loss: 1.1930
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[1m255/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4839 - loss: 1.1918
[1m287/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4842 - loss: 1.1917
[1m319/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.4843 - loss: 1.1918
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Epoch 15/28

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[1m 31/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4897 - loss: 1.1804 
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Epoch 16/28

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[1m272/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4883 - loss: 1.1728
[1m302/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4878 - loss: 1.1734
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Epoch 17/28

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[1m177/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5056 - loss: 1.1423
[1m209/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5042 - loss: 1.1442
[1m239/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5029 - loss: 1.1462
[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5018 - loss: 1.1483
[1m302/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5007 - loss: 1.1501
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4999 - loss: 1.1514 - val_accuracy: 0.5172 - val_loss: 1.1962
Epoch 18/28

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[1m 32/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5076 - loss: 1.1626 
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[1m 92/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5017 - loss: 1.1613
[1m124/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5005 - loss: 1.1612
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[1m187/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4982 - loss: 1.1606
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Epoch 19/28

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[1m 31/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5019 - loss: 1.1430 
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[1m 94/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5006 - loss: 1.1372
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Epoch 20/28

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[1m282/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4982 - loss: 1.1519
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Epoch 21/28

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[1m 92/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4971 - loss: 1.1525
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[1m183/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4993 - loss: 1.1501
[1m215/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5002 - loss: 1.1482
[1m245/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5010 - loss: 1.1467
[1m273/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5019 - loss: 1.1453
[1m300/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5023 - loss: 1.1447
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5025 - loss: 1.1443 - val_accuracy: 0.5218 - val_loss: 1.1790
Epoch 22/28

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 19ms/step - accuracy: 0.3438 - loss: 1.2266
[1m 32/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4821 - loss: 1.1043 
[1m 62/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4884 - loss: 1.1103
[1m 91/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4902 - loss: 1.1161
[1m122/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4906 - loss: 1.1218
[1m153/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4920 - loss: 1.1233
[1m183/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4936 - loss: 1.1231
[1m216/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4953 - loss: 1.1228
[1m247/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4964 - loss: 1.1231
[1m278/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4969 - loss: 1.1237
[1m308/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4973 - loss: 1.1242
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Epoch 23/28

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[1m 30/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4990 - loss: 1.1423 
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Epoch 24/28

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Epoch 25/28

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[1m 28/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5130 - loss: 1.0871 
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[1m182/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5066 - loss: 1.1167
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[1m272/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5069 - loss: 1.1179
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[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5074 - loss: 1.1175 - val_accuracy: 0.5284 - val_loss: 1.1757
Epoch 26/28

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[1m 30/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4852 - loss: 1.1240 
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[1m 91/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4906 - loss: 1.1331
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Epoch 27/28

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 23ms/step - accuracy: 0.4375 - loss: 1.1977
[1m 30/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4988 - loss: 1.1433 
[1m 60/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5087 - loss: 1.1299
[1m 87/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5113 - loss: 1.1236
[1m115/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5126 - loss: 1.1198
[1m147/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5135 - loss: 1.1180
[1m179/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5140 - loss: 1.1161
[1m209/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5148 - loss: 1.1144
[1m241/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5154 - loss: 1.1126
[1m273/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5161 - loss: 1.1111
[1m305/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5165 - loss: 1.1099
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5166 - loss: 1.1092 - val_accuracy: 0.5284 - val_loss: 1.1645
Epoch 28/28

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.4375 - loss: 1.2814
[1m 30/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5172 - loss: 1.1353 
[1m 60/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5322 - loss: 1.1127
[1m 91/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5346 - loss: 1.1039
[1m116/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5345 - loss: 1.1010
[1m145/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5341 - loss: 1.0998
[1m177/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5328 - loss: 1.0999
[1m207/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5319 - loss: 1.0992
[1m235/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5313 - loss: 1.0985
[1m267/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5302 - loss: 1.0985
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[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5284 - loss: 1.0988 - val_accuracy: 0.5320 - val_loss: 1.1661

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m23s[0m 495ms/step2025-11-07 17:05:42.655235: 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_141', 8 bytes spill stores, 8 bytes spill loads


[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 21ms/step  
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 22ms/step
Saved model to disk.
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Loaded model from disk.
(1545, 3, 250)
(10469, 3, 250)

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6:51[0m 1s/step
[1m 52/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 987us/step
[1m100/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step  
[1m163/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 932us/step
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[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m3s[0m 4ms/step

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 16ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 19ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 19ms/step

[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m59/89[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 874us/step
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 969us/step
Global accuracy score (validation) = 51.37 [%]
Global F1 score (validation) = 50.3 [%]
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[[0.2301053  0.08886481 0.5839934  0.09703661]
 [0.6313715  0.22325584 0.07215149 0.07322121]
 [0.11783764 0.40269026 0.3213602  0.15811189]
 ...
 [0.3195224  0.11854765 0.51592076 0.04600926]
 [0.2999262  0.14499208 0.39928123 0.15580049]
 [0.27971718 0.1824952  0.4585848  0.07920283]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 56.86 [%]
Global accuracy score (test) = 46.86 [%]
Global F1 score (train) = 56.34 [%]
Global F1 score (test) = 46.42 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.40      0.36      0.38       400
MODERATE-INTENSITY       0.49      0.39      0.44       400
         SEDENTARY       0.48      0.65      0.55       400
VIGOROUS-INTENSITY       0.51      0.48      0.49       345

          accuracy                           0.47      1545
         macro avg       0.47      0.47      0.46      1545
      weighted avg       0.47      0.47      0.46      1545


Accuracy capturado en la ejecución 24: 46.86 [%]
F1-score capturado en la ejecución 24: 46.42 [%]

=== EJECUCIÓN 25 ===

--- TRAIN (ejecución 25) ---

--- TEST (ejecución 25) ---
2025-11-07 17:05:53.547599: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 17:05:53.559500: 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 00:00:1762531553.573545 3351336 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762531553.577954 3351336 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762531553.588374 3351336 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762531553.588393 3351336 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762531553.588395 3351336 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762531553.588397 3351336 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 17:05:53.591699: 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.
I0000 00:00:1762531555.800586 3351336 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13758 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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Epoch 1/28
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762531558.238424 3351467 service.cc:152] XLA service 0x78e8b40149b0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762531558.238471 3351467 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-07 17:05:58.292351: 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 00:00:1762531558.605658 3351467 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762531560.895334 3351467 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m24:22[0m 4s/step - accuracy: 0.2500 - loss: 2.1293
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[1m273/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2780 - loss: 1.7942
[1m302/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.2825 - loss: 1.7819
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Epoch 2/28

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[1m 92/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3917 - loss: 1.4753
[1m124/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3919 - loss: 1.4786
[1m155/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3918 - loss: 1.4794
[1m184/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3919 - loss: 1.4789
[1m216/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3920 - loss: 1.4782
[1m246/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3921 - loss: 1.4773
[1m279/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3924 - loss: 1.4758
[1m310/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.3928 - loss: 1.4747
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Epoch 3/28

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4062 - loss: 1.5559
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[1m 91/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4110 - loss: 1.4557
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[1m181/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4103 - loss: 1.4438
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Epoch 4/28

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Epoch 5/28

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[1m274/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4407 - loss: 1.3463
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Epoch 6/28

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Epoch 7/28

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[1m182/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4439 - loss: 1.2935
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Epoch 8/28

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[1m 27/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4494 - loss: 1.2606 
[1m 59/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4650 - loss: 1.2480
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[1m238/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4740 - loss: 1.2427
[1m265/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4733 - loss: 1.2437
[1m296/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4724 - loss: 1.2454
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Epoch 9/28

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[1m 28/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4967 - loss: 1.2077 
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[1m272/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4719 - loss: 1.2333
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Epoch 10/28

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[1m 33/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4814 - loss: 1.2471 
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[1m159/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4662 - loss: 1.2426
[1m186/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4650 - loss: 1.2438
[1m219/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4643 - loss: 1.2444
[1m251/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4642 - loss: 1.2437
[1m283/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4643 - loss: 1.2427
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[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4646 - loss: 1.2412 - val_accuracy: 0.5102 - val_loss: 1.1243
Epoch 11/28

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[1m 30/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4690 - loss: 1.2172 
[1m 62/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4620 - loss: 1.2273
[1m 94/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4653 - loss: 1.2217
[1m126/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4680 - loss: 1.2183
[1m158/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4699 - loss: 1.2153
[1m190/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4710 - loss: 1.2139
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[1m285/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4721 - loss: 1.2134
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Epoch 12/28

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[1m 30/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4749 - loss: 1.2085 
[1m 61/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4838 - loss: 1.1928
[1m 90/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4858 - loss: 1.1885
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[1m186/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4878 - loss: 1.1881
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[1m246/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4875 - loss: 1.1889
[1m278/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4873 - loss: 1.1895
[1m308/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4871 - loss: 1.1900
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Epoch 13/28

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[1m 92/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4851 - loss: 1.1754
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[1m245/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4835 - loss: 1.1771
[1m275/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4833 - loss: 1.1780
[1m306/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4832 - loss: 1.1784
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Epoch 14/28

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[1m 31/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5107 - loss: 1.1469 
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[1m118/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5008 - loss: 1.1617
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[1m178/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4985 - loss: 1.1649
[1m209/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4981 - loss: 1.1663
[1m238/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4975 - loss: 1.1679
[1m267/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4972 - loss: 1.1689
[1m298/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4969 - loss: 1.1698
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4968 - loss: 1.1704
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4968 - loss: 1.1704 - val_accuracy: 0.5154 - val_loss: 1.1097
Epoch 15/28

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[1m 30/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4781 - loss: 1.1867 
[1m 60/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4798 - loss: 1.1809
[1m 92/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4801 - loss: 1.1814
[1m122/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4810 - loss: 1.1791
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Epoch 16/28

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[1m 32/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5130 - loss: 1.1364 
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[1m282/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5056 - loss: 1.1431
[1m314/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.5056 - loss: 1.1432
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Epoch 17/28

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[1m 32/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5244 - loss: 1.1142 
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[1m298/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5093 - loss: 1.1355
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[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5089 - loss: 1.1359 - val_accuracy: 0.5281 - val_loss: 1.1036
Epoch 18/28

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[1m 30/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4744 - loss: 1.1213 
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[1m121/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4841 - loss: 1.1586
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[1m182/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4889 - loss: 1.1580
[1m214/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4902 - loss: 1.1575
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[1m279/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4933 - loss: 1.1544
[1m308/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4945 - loss: 1.1531
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Epoch 19/28

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[1m 30/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4609 - loss: 1.2113 
[1m 57/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4758 - loss: 1.1861
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[1m146/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4925 - loss: 1.1591
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[1m289/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5006 - loss: 1.1448
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[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5016 - loss: 1.1430 - val_accuracy: 0.5309 - val_loss: 1.0971
Epoch 20/28

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.4688 - loss: 1.0703
[1m 27/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5010 - loss: 1.1165 
[1m 58/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5076 - loss: 1.1139
[1m 89/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5117 - loss: 1.1145
[1m121/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5143 - loss: 1.1133
[1m149/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5152 - loss: 1.1142
[1m174/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5148 - loss: 1.1161
[1m206/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5139 - loss: 1.1184
[1m237/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5137 - loss: 1.1198
[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5132 - loss: 1.1211
[1m302/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5129 - loss: 1.1223
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5127 - loss: 1.1229 - val_accuracy: 0.5256 - val_loss: 1.0993
Epoch 21/28

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.4688 - loss: 1.0521
[1m 29/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4892 - loss: 1.1445 
[1m 58/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4967 - loss: 1.1387
[1m 90/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4987 - loss: 1.1356
[1m119/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5014 - loss: 1.1311
[1m150/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5035 - loss: 1.1273
[1m177/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5051 - loss: 1.1248
[1m208/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5065 - loss: 1.1228
[1m238/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5077 - loss: 1.1213
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[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5089 - loss: 1.1201 - val_accuracy: 0.5176 - val_loss: 1.1033

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m23s[0m 488ms/step2025-11-07 17:06:20.555318: 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_141', 8 bytes spill stores, 8 bytes spill loads


[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 21ms/step  
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 21ms/step
Saved model to disk.
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Loaded model from disk.
(1545, 3, 250)
(10469, 3, 250)

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6:53[0m 1s/step
[1m 56/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 924us/step
[1m114/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 897us/step
[1m171/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 894us/step
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[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step  
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m3s[0m 4ms/step

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 16ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 18ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 18ms/step

[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 15ms/step
[1m59/89[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 866us/step
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 998us/step
Global accuracy score (validation) = 52.18 [%]
Global F1 score (validation) = 46.66 [%]
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[[0.03155437 0.05425189 0.8753614  0.0388324 ]
 [0.11202808 0.06293869 0.6948839  0.1301494 ]
 [0.27280772 0.41216755 0.09682561 0.21819909]
 ...
 [0.18297271 0.1682161  0.59270203 0.05610918]
 [0.21698508 0.16366316 0.5170937  0.10225798]
 [0.17296265 0.17038316 0.6251831  0.03147109]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 55.67 [%]
Global accuracy score (test) = 48.8 [%]
Global F1 score (train) = 52.08 [%]
Global F1 score (test) = 44.25 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.48      0.08      0.13       400
MODERATE-INTENSITY       0.45      0.64      0.53       400
         SEDENTARY       0.47      0.79      0.59       400
VIGOROUS-INTENSITY       0.63      0.44      0.52       345

          accuracy                           0.49      1545
         macro avg       0.51      0.49      0.44      1545
      weighted avg       0.50      0.49      0.44      1545


Accuracy capturado en la ejecución 25: 48.8 [%]
F1-score capturado en la ejecución 25: 44.25 [%]

=== EJECUCIÓN 26 ===

--- TRAIN (ejecución 26) ---

--- TEST (ejecución 26) ---
2025-11-07 17:06:31.516020: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 17:06:31.527777: 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 00:00:1762531591.541792 3354311 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762531591.546104 3354311 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762531591.556362 3354311 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762531591.556384 3354311 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762531591.556386 3354311 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762531591.556388 3354311 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 17:06:31.559733: 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.
I0000 00:00:1762531593.789017 3354311 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13758 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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Epoch 1/28
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762531596.259593 3354447 service.cc:152] XLA service 0x7ed6c8004fd0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762531596.259624 3354447 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-07 17:06:36.310199: 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 00:00:1762531596.623699 3354447 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762531598.912940 3354447 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

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[1m271/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3012 - loss: 1.8389
[1m301/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.3047 - loss: 1.8268
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Epoch 2/28

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[1m124/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3789 - loss: 1.5625
[1m149/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3790 - loss: 1.5575
[1m175/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3787 - loss: 1.5549
[1m203/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3783 - loss: 1.5532
[1m234/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3784 - loss: 1.5504
[1m265/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3785 - loss: 1.5473
[1m297/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.3787 - loss: 1.5445
[1m326/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.3790 - loss: 1.5416
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3790 - loss: 1.5413 - val_accuracy: 0.4379 - val_loss: 1.2463
Epoch 3/28

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[1m155/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3893 - loss: 1.4957
[1m186/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3907 - loss: 1.4926
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Epoch 4/28

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Epoch 5/28

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Epoch 6/28

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Epoch 7/28

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Epoch 8/28

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[1m273/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4552 - loss: 1.2772
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Epoch 9/28

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[1m246/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4662 - loss: 1.2501
[1m277/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4655 - loss: 1.2501
[1m309/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4649 - loss: 1.2501
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Epoch 10/28

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[1m124/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4773 - loss: 1.2183
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[1m184/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4767 - loss: 1.2207
[1m215/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4757 - loss: 1.2235
[1m241/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4749 - loss: 1.2253
[1m272/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4740 - loss: 1.2274
[1m304/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4734 - loss: 1.2290
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4731 - loss: 1.2299 - val_accuracy: 0.4986 - val_loss: 1.1491
Epoch 11/28

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[1m 29/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4453 - loss: 1.2478 
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[1m 89/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4501 - loss: 1.2542
[1m120/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4500 - loss: 1.2558
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[1m181/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4524 - loss: 1.2540
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[1m247/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4540 - loss: 1.2504
[1m278/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4545 - loss: 1.2493
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Epoch 12/28

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[1m 32/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4384 - loss: 1.2764 
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Epoch 13/28

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[1m274/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4765 - loss: 1.1982
[1m306/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4759 - loss: 1.1987
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Epoch 14/28

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[1m197/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4943 - loss: 1.1708
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[1m260/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4928 - loss: 1.1741
[1m292/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4922 - loss: 1.1749
[1m323/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.4915 - loss: 1.1757
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4914 - loss: 1.1758 - val_accuracy: 0.5028 - val_loss: 1.1357
Epoch 15/28

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[1m 32/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4792 - loss: 1.1647 
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[1m 93/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4841 - loss: 1.1654
[1m123/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4832 - loss: 1.1690
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[1m184/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4828 - loss: 1.1751
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[1m273/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4827 - loss: 1.1784
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Epoch 16/28

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Epoch 17/28

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[1m268/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4945 - loss: 1.1665
[1m299/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4945 - loss: 1.1656
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Epoch 18/28

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[1m119/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4813 - loss: 1.1953
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[1m181/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4835 - loss: 1.1868
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[1m271/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4850 - loss: 1.1802
[1m301/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4855 - loss: 1.1786
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Epoch 19/28

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[1m 32/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4954 - loss: 1.1953 
[1m 62/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4889 - loss: 1.1918
[1m 92/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4907 - loss: 1.1814
[1m118/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4921 - loss: 1.1743
[1m149/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4930 - loss: 1.1684
[1m181/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4938 - loss: 1.1636
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[1m244/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4955 - loss: 1.1569
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Epoch 20/28

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[1m 28/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4929 - loss: 1.1341 
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[1m 89/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4924 - loss: 1.1402
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[1m184/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4960 - loss: 1.1358
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[1m276/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4985 - loss: 1.1345
[1m304/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4987 - loss: 1.1346
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Epoch 21/28

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.3750 - loss: 1.4410
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[1m279/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4930 - loss: 1.1465
[1m308/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4942 - loss: 1.1445
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Epoch 22/28

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[1m 31/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5007 - loss: 1.0879 
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[1m121/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5004 - loss: 1.1130
[1m150/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5005 - loss: 1.1167
[1m180/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5002 - loss: 1.1193
[1m209/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5004 - loss: 1.1207
[1m238/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5008 - loss: 1.1216
[1m269/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5008 - loss: 1.1225
[1m299/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5007 - loss: 1.1236
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5006 - loss: 1.1247 - val_accuracy: 0.5014 - val_loss: 1.1195
Epoch 23/28

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.4062 - loss: 1.2790
[1m 31/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5151 - loss: 1.0939 
[1m 63/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5151 - loss: 1.0970
[1m 95/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5129 - loss: 1.1007
[1m127/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5133 - loss: 1.1024
[1m161/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5140 - loss: 1.1037
[1m191/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5143 - loss: 1.1042
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[1m251/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5154 - loss: 1.1040
[1m281/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5155 - loss: 1.1044
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Epoch 24/28

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[1m 33/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5046 - loss: 1.1399 
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Epoch 25/28

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[1m305/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5175 - loss: 1.0994
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Epoch 26/28

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[1m 26/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5225 - loss: 1.1107 
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[1m182/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5201 - loss: 1.1028
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[1m240/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5205 - loss: 1.1034
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[1m304/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5204 - loss: 1.1044
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5202 - loss: 1.1047 - val_accuracy: 0.4958 - val_loss: 1.1218
Epoch 27/28

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[1m 31/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5198 - loss: 1.0677 
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[1m 87/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5150 - loss: 1.0939
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[1m178/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5160 - loss: 1.0966
[1m209/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5165 - loss: 1.0961
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[1m273/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5170 - loss: 1.0957
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Epoch 28/28

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[1m 28/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5101 - loss: 1.0776 
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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m24s[0m 510ms/step2025-11-07 17:07:03.604934: 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_141', 8 bytes spill stores, 8 bytes spill loads

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Saved model to disk.
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Loaded model from disk.
(1545, 3, 250)
(10469, 3, 250)

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7:12[0m 1s/step
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[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 15ms/step
[1m56/89[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 913us/step
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step  
Global accuracy score (validation) = 51.65 [%]
Global F1 score (validation) = 51.57 [%]
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[[0.17163177 0.0858698  0.6298311  0.11266736]
 [0.56056386 0.12202546 0.3098382  0.00757252]
 [0.5178665  0.13333453 0.3413495  0.00744944]
 ...
 [0.30719644 0.19555895 0.38923824 0.10800641]
 [0.2997261  0.19524087 0.3830779  0.12195511]
 [0.3120709  0.16489431 0.46771416 0.05532062]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 56.45 [%]
Global accuracy score (test) = 52.1 [%]
Global F1 score (train) = 56.7 [%]
Global F1 score (test) = 52.19 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.39      0.40      0.40       400
MODERATE-INTENSITY       0.54      0.56      0.55       400
         SEDENTARY       0.52      0.67      0.59       400
VIGOROUS-INTENSITY       0.75      0.44      0.56       345

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


Accuracy capturado en la ejecución 26: 52.1 [%]
F1-score capturado en la ejecución 26: 52.19 [%]

=== EJECUCIÓN 27 ===

--- TRAIN (ejecución 27) ---

--- TEST (ejecución 27) ---
2025-11-07 17:07:14.652706: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 17:07:14.663941: 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 00:00:1762531634.677049 3357987 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762531634.681182 3357987 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762531634.691496 3357987 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762531634.691516 3357987 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762531634.691518 3357987 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762531634.691520 3357987 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 17:07:14.694503: 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.
I0000 00:00:1762531636.949544 3357987 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13758 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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Epoch 1/28
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762531639.429958 3358117 service.cc:152] XLA service 0x7b818c015280 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762531639.430003 3358117 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-07 17:07:19.483869: 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 00:00:1762531639.797155 3358117 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762531642.103787 3358117 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 2/28

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Epoch 3/28

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[1m 28/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4296 - loss: 1.4437 
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[1m271/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3957 - loss: 1.5062
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Epoch 4/28

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[1m 28/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3576 - loss: 1.5784 
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[1m 86/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3749 - loss: 1.5190
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[1m149/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3847 - loss: 1.4930
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[1m239/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3895 - loss: 1.4768
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Epoch 5/28

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[1m 28/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4134 - loss: 1.4543 
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[1m239/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4196 - loss: 1.4123
[1m273/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4202 - loss: 1.4103
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Epoch 6/28

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[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4279 - loss: 1.3582
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Epoch 7/28

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[1m245/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4451 - loss: 1.3160
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Epoch 8/28

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[1m 26/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4309 - loss: 1.3718 
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[1m 92/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4360 - loss: 1.3293
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Epoch 9/28

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Epoch 10/28

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[1m247/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4509 - loss: 1.2630
[1m279/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4506 - loss: 1.2640
[1m305/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4504 - loss: 1.2646
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Epoch 11/28

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[1m130/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4570 - loss: 1.2472
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[1m187/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4610 - loss: 1.2428
[1m216/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4620 - loss: 1.2417
[1m247/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4627 - loss: 1.2412
[1m274/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4629 - loss: 1.2412
[1m306/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4628 - loss: 1.2416
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4628 - loss: 1.2416 - val_accuracy: 0.4698 - val_loss: 1.2387
Epoch 12/28

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[1m 30/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4760 - loss: 1.2553 
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[1m 95/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4625 - loss: 1.2477
[1m127/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4609 - loss: 1.2449
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[1m186/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4596 - loss: 1.2431
[1m216/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4592 - loss: 1.2429
[1m250/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4587 - loss: 1.2430
[1m283/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4583 - loss: 1.2431
[1m316/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.4580 - loss: 1.2431
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Epoch 13/28

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Epoch 14/28

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Epoch 15/28

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[1m183/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4806 - loss: 1.2051
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Epoch 16/28

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[1m 32/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4703 - loss: 1.2393 
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[1m180/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4690 - loss: 1.2167
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Epoch 17/28

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[1m 27/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4882 - loss: 1.1589 
[1m 59/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4932 - loss: 1.1578
[1m 89/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4918 - loss: 1.1608
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[1m216/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4882 - loss: 1.1652
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[1m276/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4875 - loss: 1.1668
[1m308/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4875 - loss: 1.1672
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Epoch 18/28

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[1m 27/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4911 - loss: 1.1490 
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[1m 90/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4943 - loss: 1.1520
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[1m237/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4927 - loss: 1.1576
[1m268/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4924 - loss: 1.1584
[1m296/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4921 - loss: 1.1591
[1m327/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.4919 - loss: 1.1597
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Epoch 19/28

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[1m 32/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4798 - loss: 1.2124 
[1m 62/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4805 - loss: 1.2012
[1m 93/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4803 - loss: 1.1927
[1m122/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4800 - loss: 1.1877
[1m154/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4804 - loss: 1.1835
[1m184/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4813 - loss: 1.1804
[1m216/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4824 - loss: 1.1777
[1m246/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4833 - loss: 1.1757
[1m279/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4842 - loss: 1.1741
[1m309/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4848 - loss: 1.1731
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4853 - loss: 1.1724 - val_accuracy: 0.4891 - val_loss: 1.1878
Epoch 20/28

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[1m 31/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5013 - loss: 1.1648 
[1m 61/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4991 - loss: 1.1613
[1m 90/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4978 - loss: 1.1578
[1m121/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4966 - loss: 1.1549
[1m152/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4959 - loss: 1.1533
[1m184/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4950 - loss: 1.1531
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[1m280/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4939 - loss: 1.1523
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Epoch 21/28

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[1m 31/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4765 - loss: 1.1624 
[1m 61/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4789 - loss: 1.1703
[1m 92/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4832 - loss: 1.1701
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[1m250/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4893 - loss: 1.1630
[1m280/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4899 - loss: 1.1619
[1m312/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.4906 - loss: 1.1607
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Epoch 22/28

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[1m 31/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5271 - loss: 1.0978 
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[1m186/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5130 - loss: 1.1165
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[1m282/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5103 - loss: 1.1235
[1m311/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5094 - loss: 1.1255
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Epoch 23/28

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[1m 33/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5329 - loss: 1.1009 
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[1m126/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5076 - loss: 1.1374
[1m157/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5046 - loss: 1.1413
[1m186/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5029 - loss: 1.1428
[1m218/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5024 - loss: 1.1426
[1m251/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5024 - loss: 1.1417
[1m278/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5026 - loss: 1.1408
[1m311/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5029 - loss: 1.1397
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5030 - loss: 1.1393 - val_accuracy: 0.4912 - val_loss: 1.1761
Epoch 24/28

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5938 - loss: 0.9957
[1m 32/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5273 - loss: 1.0653 
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[1m 95/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5328 - loss: 1.0773
[1m126/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5318 - loss: 1.0808
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[1m189/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5287 - loss: 1.0865
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[1m277/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5250 - loss: 1.0938
[1m309/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5243 - loss: 1.0957
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Epoch 25/28

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Epoch 26/28

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Epoch 27/28

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[1m184/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5091 - loss: 1.0753
[1m217/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5096 - loss: 1.0783
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Epoch 28/28

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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m22s[0m 475ms/step2025-11-07 17:07:46.771301: 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_141', 8 bytes spill stores, 8 bytes spill loads

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Saved model to disk.
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Loaded model from disk.
(1545, 3, 250)
(10469, 3, 250)

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7:07[0m 1s/step
[1m 52/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 984us/step
[1m112/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 910us/step
[1m173/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 882us/step
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[1m289/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 879us/step
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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 16ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 18ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 19ms/step

[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 15ms/step
[1m57/89[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 902us/step
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step  
Global accuracy score (validation) = 51.65 [%]
Global F1 score (validation) = 51.18 [%]
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[[0.2553235  0.4148     0.08275341 0.24712305]
 [0.3290687  0.11173619 0.36346808 0.19572711]
 [0.3054846  0.11589721 0.44076478 0.1378534 ]
 ...
 [0.36998358 0.14865473 0.38910487 0.09225678]
 [0.34711233 0.12079951 0.4360635  0.09602465]
 [0.349393   0.20331511 0.38448516 0.06280667]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 57.84 [%]
Global accuracy score (test) = 51.59 [%]
Global F1 score (train) = 57.98 [%]
Global F1 score (test) = 51.73 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.42      0.42      0.42       400
MODERATE-INTENSITY       0.51      0.55      0.53       400
         SEDENTARY       0.52      0.62      0.57       400
VIGOROUS-INTENSITY       0.69      0.46      0.55       345

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


Accuracy capturado en la ejecución 27: 51.59 [%]
F1-score capturado en la ejecución 27: 51.73 [%]

=== EJECUCIÓN 28 ===

--- TRAIN (ejecución 28) ---

--- TEST (ejecución 28) ---
2025-11-07 17:07:57.808720: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 17:07:57.820181: 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 00:00:1762531677.833479 3361639 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762531677.837659 3361639 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762531677.847655 3361639 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762531677.847676 3361639 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762531677.847678 3361639 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762531677.847679 3361639 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 17:07:57.850936: 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.
I0000 00:00:1762531680.089696 3361639 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13758 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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Epoch 1/28
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762531682.578652 3361771 service.cc:152] XLA service 0x723cc00026c0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762531682.578691 3361771 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-07 17:08:02.631465: 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 00:00:1762531682.964818 3361771 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762531685.327603 3361771 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m25:09[0m 5s/step - accuracy: 0.1562 - loss: 1.9750
[1m 27/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2395 - loss: 1.8134  
[1m 58/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2539 - loss: 1.7919
[1m 88/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2618 - loss: 1.7758
[1m121/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2678 - loss: 1.7605
[1m153/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2742 - loss: 1.7439
[1m185/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2798 - loss: 1.7297
[1m214/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2845 - loss: 1.7175
[1m244/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2886 - loss: 1.7061
[1m273/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2922 - loss: 1.6964
[1m307/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.2962 - loss: 1.6860
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step - accuracy: 0.2985 - loss: 1.6801
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m9s[0m 13ms/step - accuracy: 0.2986 - loss: 1.6798 - val_accuracy: 0.4940 - val_loss: 1.2058
Epoch 2/28

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[1m 28/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4131 - loss: 1.4126 
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Epoch 3/28

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Epoch 4/28

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Epoch 5/28

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Epoch 6/28

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Epoch 7/28

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Epoch 8/28

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[1m180/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4618 - loss: 1.2566
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[1m273/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4587 - loss: 1.2550
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Epoch 9/28

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Epoch 10/28

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[1m 31/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4444 - loss: 1.2696 
[1m 62/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4507 - loss: 1.2525
[1m 94/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4535 - loss: 1.2458
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[1m251/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4613 - loss: 1.2294
[1m284/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4626 - loss: 1.2265
[1m313/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.4635 - loss: 1.2250
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Epoch 11/28

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[1m 30/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4962 - loss: 1.1968 
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[1m118/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4872 - loss: 1.2048
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[1m242/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4828 - loss: 1.2030
[1m275/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4819 - loss: 1.2029
[1m305/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4810 - loss: 1.2027
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Epoch 12/28

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[1m 30/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4614 - loss: 1.2650 
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[1m119/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4707 - loss: 1.2124
[1m146/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4718 - loss: 1.2081
[1m174/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4723 - loss: 1.2052
[1m203/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4727 - loss: 1.2028
[1m232/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4730 - loss: 1.2007
[1m261/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4733 - loss: 1.1989
[1m293/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4736 - loss: 1.1975
[1m324/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.4738 - loss: 1.1964
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4738 - loss: 1.1963 - val_accuracy: 0.5358 - val_loss: 1.1230
Epoch 13/28

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[1m 32/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4974 - loss: 1.1824 
[1m 63/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4920 - loss: 1.1910
[1m 92/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4843 - loss: 1.1974
[1m121/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4809 - loss: 1.1994
[1m153/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4792 - loss: 1.2000
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[1m271/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4785 - loss: 1.1955
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Epoch 14/28

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[1m 33/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4948 - loss: 1.1554 
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[1m284/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4864 - loss: 1.1663
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Epoch 15/28

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Epoch 16/28

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[1m 28/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5145 - loss: 1.1347 
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[1m269/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4861 - loss: 1.1640
[1m298/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4864 - loss: 1.1628
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Epoch 17/28

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[1m 31/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4704 - loss: 1.1432 
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Epoch 18/28

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[1m 32/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5058 - loss: 1.1141 
[1m 64/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5179 - loss: 1.1128
[1m 96/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5188 - loss: 1.1166
[1m129/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5184 - loss: 1.1198
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[1m191/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5164 - loss: 1.1246
[1m224/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5152 - loss: 1.1265
[1m255/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5141 - loss: 1.1278
[1m283/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5133 - loss: 1.1285
[1m314/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.5125 - loss: 1.1291
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Epoch 19/28

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[1m 31/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5256 - loss: 1.1305 
[1m 61/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5160 - loss: 1.1278
[1m 90/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5102 - loss: 1.1318
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[1m149/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5053 - loss: 1.1371
[1m178/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5041 - loss: 1.1386
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[1m241/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5022 - loss: 1.1401
[1m269/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5021 - loss: 1.1399
[1m301/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5022 - loss: 1.1390
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5023 - loss: 1.1382 - val_accuracy: 0.5351 - val_loss: 1.1118
Epoch 20/28

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[1m 31/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5275 - loss: 1.0826 
[1m 62/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5054 - loss: 1.1084
[1m 90/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4988 - loss: 1.1190
[1m122/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4961 - loss: 1.1247
[1m151/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4952 - loss: 1.1275
[1m184/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4951 - loss: 1.1289
[1m216/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4952 - loss: 1.1297
[1m250/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4956 - loss: 1.1301
[1m281/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4961 - loss: 1.1300
[1m314/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.4966 - loss: 1.1298
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4969 - loss: 1.1296 - val_accuracy: 0.5309 - val_loss: 1.1079
Epoch 21/28

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[1m 33/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5033 - loss: 1.1095 
[1m 65/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5084 - loss: 1.1077
[1m 94/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5115 - loss: 1.1074
[1m127/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5138 - loss: 1.1070
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[1m188/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5155 - loss: 1.1087
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[1m280/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5151 - loss: 1.1121
[1m307/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5151 - loss: 1.1126
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Epoch 22/28

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[1m 32/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4611 - loss: 1.2028 
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Epoch 23/28

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[1m296/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5058 - loss: 1.1149
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Epoch 24/28

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[1m183/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5237 - loss: 1.0896
[1m214/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5225 - loss: 1.0911
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[1m278/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5219 - loss: 1.0925
[1m306/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5220 - loss: 1.0927
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5220 - loss: 1.0929 - val_accuracy: 0.5316 - val_loss: 1.1005
Epoch 25/28

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[1m 28/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5205 - loss: 1.1045 
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[1m125/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5141 - loss: 1.1040
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[1m186/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5164 - loss: 1.1009
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[1m278/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5180 - loss: 1.0972
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Epoch 26/28

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Epoch 27/28

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Epoch 28/28

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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m23s[0m 492ms/step2025-11-07 17:08:30.225807: 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_141', 8 bytes spill stores, 8 bytes spill loads


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Saved model to disk.
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Loaded model from disk.
(1545, 3, 250)
(10469, 3, 250)

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[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step  
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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 15ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 19ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 19ms/step

[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 15ms/step
[1m56/89[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 915us/step
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step  
Global accuracy score (validation) = 49.54 [%]
Global F1 score (validation) = 46.74 [%]
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[[0.22728239 0.29647955 0.437872   0.038366  ]
 [0.20056581 0.1395734  0.5321669  0.12769388]
 [0.25974745 0.3467077  0.10188398 0.29166093]
 ...
 [0.28064343 0.11992838 0.50958896 0.08983924]
 [0.23405954 0.09066596 0.5668576  0.10841691]
 [0.31357267 0.12874217 0.5031354  0.05454984]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 55.54 [%]
Global accuracy score (test) = 45.11 [%]
Global F1 score (train) = 53.9 [%]
Global F1 score (test) = 42.8 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.38      0.18      0.24       400
MODERATE-INTENSITY       0.43      0.36      0.40       400
         SEDENTARY       0.46      0.75      0.57       400
VIGOROUS-INTENSITY       0.48      0.52      0.50       345

          accuracy                           0.45      1545
         macro avg       0.44      0.45      0.43      1545
      weighted avg       0.44      0.45      0.43      1545


Accuracy capturado en la ejecución 28: 45.11 [%]
F1-score capturado en la ejecución 28: 42.8 [%]

=== EJECUCIÓN 29 ===

--- TRAIN (ejecución 29) ---

--- TEST (ejecución 29) ---
2025-11-07 17:08:41.217559: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 17:08:41.228872: 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 00:00:1762531721.241877 3365315 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762531721.245978 3365315 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762531721.255748 3365315 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762531721.255769 3365315 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762531721.255771 3365315 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762531721.255772 3365315 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 17:08:41.259030: 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.
I0000 00:00:1762531723.487539 3365315 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13758 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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Epoch 1/28
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762531725.951056 3365448 service.cc:152] XLA service 0x72fb54001c40 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762531725.951089 3365448 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-07 17:08:46.001004: 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 00:00:1762531726.317383 3365448 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762531728.603425 3365448 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m24:23[0m 4s/step - accuracy: 0.3125 - loss: 1.8619
[1m 31/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2693 - loss: 1.9158  
[1m 60/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2754 - loss: 1.8911
[1m 92/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2845 - loss: 1.8614
[1m118/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2903 - loss: 1.8427
[1m150/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2962 - loss: 1.8232
[1m181/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3016 - loss: 1.8075
[1m209/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3057 - loss: 1.7958
[1m240/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3093 - loss: 1.7848
[1m273/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3129 - loss: 1.7741
[1m301/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.3154 - loss: 1.7662
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step - accuracy: 0.3174 - loss: 1.7593
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m9s[0m 13ms/step - accuracy: 0.3175 - loss: 1.7591 - val_accuracy: 0.4831 - val_loss: 1.2929
Epoch 2/28

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[1m 29/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3489 - loss: 1.5913 
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[1m 91/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3633 - loss: 1.5701
[1m122/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3664 - loss: 1.5637
[1m153/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3691 - loss: 1.5594
[1m180/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3711 - loss: 1.5561
[1m209/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3731 - loss: 1.5520
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[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3757 - loss: 1.5449
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Epoch 3/28

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Epoch 4/28

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Epoch 5/28

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[1m177/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4164 - loss: 1.3828
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Epoch 6/28

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[1m 27/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4439 - loss: 1.3596 
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Epoch 7/28

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Epoch 8/28

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[1m244/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4558 - loss: 1.2684
[1m272/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4547 - loss: 1.2699
[1m304/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4537 - loss: 1.2714
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Epoch 9/28

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[1m123/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4480 - loss: 1.3083
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[1m184/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4472 - loss: 1.3012
[1m215/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4470 - loss: 1.2990
[1m244/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4468 - loss: 1.2976
[1m275/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4469 - loss: 1.2960
[1m304/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4468 - loss: 1.2947
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4468 - loss: 1.2935 - val_accuracy: 0.4989 - val_loss: 1.1910
Epoch 10/28

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[1m 30/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4220 - loss: 1.2873 
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[1m 90/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4410 - loss: 1.2788
[1m120/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4443 - loss: 1.2730
[1m149/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4453 - loss: 1.2706
[1m179/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4457 - loss: 1.2703
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[1m239/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4466 - loss: 1.2699
[1m267/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4473 - loss: 1.2693
[1m298/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4478 - loss: 1.2689
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Epoch 11/28

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[1m309/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4642 - loss: 1.2216
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Epoch 12/28

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Epoch 13/28

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[1m184/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4754 - loss: 1.1963
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[1m273/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4741 - loss: 1.2007
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[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4739 - loss: 1.2014 - val_accuracy: 0.4993 - val_loss: 1.1778
Epoch 14/28

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[1m 26/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4641 - loss: 1.1916 
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[1m118/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4668 - loss: 1.2018
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[1m182/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4686 - loss: 1.2052
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[1m296/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4699 - loss: 1.2072
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Epoch 15/28

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[1m 32/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4706 - loss: 1.1835 
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Epoch 16/28

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Epoch 17/28

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[1m247/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4984 - loss: 1.1627
[1m279/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4981 - loss: 1.1629
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Epoch 18/28

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[1m 32/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4913 - loss: 1.1483 
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Epoch 19/28

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[1m 28/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5270 - loss: 1.1090 
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[1m 91/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5124 - loss: 1.1294
[1m120/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5090 - loss: 1.1340
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[1m179/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5052 - loss: 1.1392
[1m209/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5038 - loss: 1.1412
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[1m268/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5019 - loss: 1.1439
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Epoch 20/28

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[1m 28/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4973 - loss: 1.1206 
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[1m298/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4896 - loss: 1.1473
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Epoch 21/28

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4375 - loss: 1.2817
[1m 30/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5052 - loss: 1.1206 
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[1m 90/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5084 - loss: 1.1274
[1m122/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5072 - loss: 1.1300
[1m149/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5071 - loss: 1.1304
[1m178/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5064 - loss: 1.1314
[1m208/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5061 - loss: 1.1327
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[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5060 - loss: 1.1342
[1m304/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5058 - loss: 1.1350
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5058 - loss: 1.1352 - val_accuracy: 0.5116 - val_loss: 1.1722
Epoch 22/28

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.4062 - loss: 1.2906
[1m 29/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4840 - loss: 1.1658 
[1m 60/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4960 - loss: 1.1465
[1m 90/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4999 - loss: 1.1376
[1m122/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5021 - loss: 1.1324
[1m156/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5036 - loss: 1.1305
[1m187/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5044 - loss: 1.1296
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[1m245/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5049 - loss: 1.1292
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[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5051 - loss: 1.1301 - val_accuracy: 0.4982 - val_loss: 1.1708
Epoch 23/28

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[1m 31/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4782 - loss: 1.1625 
[1m 62/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4893 - loss: 1.1497
[1m 95/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4945 - loss: 1.1414
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[1m247/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4993 - loss: 1.1370
[1m276/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4993 - loss: 1.1369
[1m308/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4993 - loss: 1.1369
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Epoch 24/28

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[1m 33/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4895 - loss: 1.1098 
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[1m184/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5014 - loss: 1.1132
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[1m274/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5040 - loss: 1.1139
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Epoch 25/28

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[1m 30/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4964 - loss: 1.1312 
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[1m125/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5094 - loss: 1.1129
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[1m184/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5113 - loss: 1.1100
[1m216/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5118 - loss: 1.1093
[1m248/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5121 - loss: 1.1090
[1m280/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5125 - loss: 1.1086
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Epoch 26/28

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[1m 31/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5470 - loss: 1.0902 
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[1m 90/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5427 - loss: 1.0934
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[1m308/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5285 - loss: 1.1004
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Epoch 27/28

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[1m 29/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5201 - loss: 1.1149 
[1m 58/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5211 - loss: 1.1127
[1m 89/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5216 - loss: 1.1105
[1m117/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5228 - loss: 1.1063
[1m147/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5225 - loss: 1.1047
[1m176/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5228 - loss: 1.1037
[1m205/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5228 - loss: 1.1030
[1m236/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5228 - loss: 1.1025
[1m266/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5231 - loss: 1.1016
[1m293/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5233 - loss: 1.1006
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[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5235 - loss: 1.0996 - val_accuracy: 0.5172 - val_loss: 1.1528
Epoch 28/28

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5312 - loss: 1.1244
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[1m 63/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5142 - loss: 1.0773
[1m 93/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5135 - loss: 1.0792
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[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5157 - loss: 1.0901 - val_accuracy: 0.5169 - val_loss: 1.1536

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m23s[0m 489ms/step2025-11-07 17:09:13.503283: 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_141', 8 bytes spill stores, 8 bytes spill loads


[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 22ms/step  
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 23ms/step
Saved model to disk.
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Loaded model from disk.
(1545, 3, 250)
(10469, 3, 250)

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6:56[0m 1s/step
[1m 53/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 973us/step
[1m111/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 918us/step
[1m176/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 865us/step
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[1m292/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 866us/step
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step  
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m3s[0m 4ms/step

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 17ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 18ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 19ms/step

[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 21ms/step
[1m60/89[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 851us/step
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 992us/step
Global accuracy score (validation) = 52.32 [%]
Global F1 score (validation) = 50.77 [%]
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[[0.4948194  0.24216355 0.24402678 0.01899019]
 [0.03575174 0.02383002 0.90659606 0.03382225]
 [0.26063365 0.07892863 0.544881   0.11555678]
 ...
 [0.25800067 0.17479305 0.4959023  0.07130394]
 [0.30165002 0.12553197 0.48651293 0.08630506]
 [0.2091009  0.15954503 0.5806913  0.05066276]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 58.58 [%]
Global accuracy score (test) = 52.36 [%]
Global F1 score (train) = 58.05 [%]
Global F1 score (test) = 51.21 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.39      0.24      0.29       400
MODERATE-INTENSITY       0.49      0.63      0.55       400
         SEDENTARY       0.53      0.72      0.61       400
VIGOROUS-INTENSITY       0.70      0.51      0.59       345

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


Accuracy capturado en la ejecución 29: 52.36 [%]
F1-score capturado en la ejecución 29: 51.21 [%]

=== EJECUCIÓN 30 ===

--- TRAIN (ejecución 30) ---

--- TEST (ejecución 30) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Loaded model from disk.
(1545, 3, 250)
(10469, 3, 250)

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7:03[0m 1s/step
[1m 59/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 882us/step
[1m116/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 888us/step
[1m176/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 874us/step
[1m228/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 897us/step
[1m290/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 878us/step
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step  
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m3s[0m 4ms/step

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 16ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 19ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 20ms/step

[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m60/89[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 849us/step
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step  
Global accuracy score (validation) = 51.58 [%]
Global F1 score (validation) = 48.37 [%]
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[[0.17704444 0.08131404 0.64268446 0.09895706]
 [0.13931796 0.51405454 0.27507123 0.07155631]
 [0.4049195  0.18786763 0.3999762  0.00723668]
 ...
 [0.32592794 0.12118726 0.50513333 0.04775147]
 [0.2804941  0.11052228 0.5505356  0.05844796]
 [0.3046348  0.22806491 0.4130032  0.05429709]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 57.67 [%]
Global accuracy score (test) = 49.64 [%]
Global F1 score (train) = 56.19 [%]
Global F1 score (test) = 46.78 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.36      0.13      0.19       400
MODERATE-INTENSITY       0.49      0.65      0.55       400
         SEDENTARY       0.48      0.71      0.57       400
VIGOROUS-INTENSITY       0.62      0.50      0.56       345

          accuracy                           0.50      1545
         macro avg       0.49      0.50      0.47      1545
      weighted avg       0.48      0.50      0.46      1545


Accuracy capturado en la ejecución 30: 49.64 [%]
F1-score capturado en la ejecución 30: 46.78 [%]

=== RESUMEN FINAL ===
Accuracies: [52.56, 48.22, 49.97, 50.74, 50.42, 50.1, 48.61, 50.42, 51.91, 48.74, 49.71, 51.78, 52.56, 47.64, 50.81, 48.16, 49.39, 48.09, 50.61, 52.04, 48.48, 48.48, 48.03, 46.86, 48.8, 52.1, 51.59, 45.11, 52.36, 49.64]
F1-scores: [48.41, 46.91, 48.33, 49.0, 49.8, 49.06, 46.17, 49.05, 49.12, 46.48, 49.53, 50.95, 49.43, 47.4, 47.65, 47.64, 46.26, 46.72, 49.28, 47.31, 48.07, 47.9, 48.11, 46.42, 44.25, 52.19, 51.73, 42.8, 51.21, 46.78]
Accuracy mean: 49.7977 | std: 1.8339
F1 mean: 48.1320 | std: 2.0140

Resultados guardados en /mnt/nvme1n2/git/uniovi-simur-wearablepermed-data/output/Paper_results/cases_dataset_M/case_M_CAPTURE24_acc_superclasses_CPA_METs/metrics_test.npz
