2025-11-04 14:57:41.998185: 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`.
2025-11-04 14:57:42.009832: 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 00:00:1762264662.024044 1857190 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762264662.028298 1857190 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762264662.038963 1857190 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762264662.038984 1857190 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762264662.038986 1857190 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762264662.038988 1857190 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-04 14:57:42.042115: 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(
2025-11-04 14:57:45,143	INFO worker.py:1927 -- Started a local Ray instance.
2025-11-04 14:57:45,880	INFO tune.py:253 -- Initializing Ray automatically. For cluster usage or custom Ray initialization, call `ray.init(...)` before `Tuner(...)`.
2025-11-04 14:57:45,947	INFO trial.py:182 -- Creating a new dirname dir_3d9be_073e because trial dirname 'dir_3d9be' already exists.
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2025-11-04 14:57:45,975	INFO trial.py:182 -- Creating a new dirname dir_3d9be_abef because trial dirname 'dir_3d9be' already exists.
2025-11-04 14:57:45,978	INFO trial.py:182 -- Creating a new dirname dir_3d9be_57fe because trial dirname 'dir_3d9be' already exists.
2025-11-04 14:57:45,980	INFO trial.py:182 -- Creating a new dirname dir_3d9be_a079 because trial dirname 'dir_3d9be' already exists.
2025-11-04 14:57:45,984	INFO trial.py:182 -- Creating a new dirname dir_3d9be_b987 because trial dirname 'dir_3d9be' already exists.
2025-11-04 14:57:45,987	INFO trial.py:182 -- Creating a new dirname dir_3d9be_65db because trial dirname 'dir_3d9be' already exists.
2025-11-04 14:57:45,991	INFO trial.py:182 -- Creating a new dirname dir_3d9be_3b2c because trial dirname 'dir_3d9be' already exists.
2025-11-04 14:57:45,994	INFO trial.py:182 -- Creating a new dirname dir_3d9be_93b3 because trial dirname 'dir_3d9be' already exists.
2025-11-04 14:57:45,998	INFO trial.py:182 -- Creating a new dirname dir_3d9be_3eb5 because trial dirname 'dir_3d9be' already exists.
2025-11-04 14:57:46,002	INFO trial.py:182 -- Creating a new dirname dir_3d9be_f839 because trial dirname 'dir_3d9be' already exists.
2025-11-04 14:57:46,012	INFO trial.py:182 -- Creating a new dirname dir_3d9be_4ef7 because trial dirname 'dir_3d9be' 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_C/case_C_CAPTURE24_acc_gyr_superclasses_CPA_METs/CAPTURE24_hyperparameters_tuning
To visualize your results with TensorBoard, run: `tensorboard --logdir /tmp/ray/session_2025-11-04_14-57-44_442387_1857190/artifacts/2025-11-04_14-57-45/CAPTURE24_hyperparameters_tuning/driver_artifacts`

Trial status: 20 PENDING
Current time: 2025-11-04 14:57:46. Total running time: 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 │
├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_3d9be    PENDING            3   rmsprop         tanh                                   16                 16                  3                 0          9.30537e-05         19 │
│ trial_3d9be    PENDING            2   adam            tanh                                   16                 32                  5                 0          8.54171e-06         25 │
│ trial_3d9be    PENDING            2   rmsprop         relu                                   32                 16                  5                 1          1.02234e-05         27 │
│ trial_3d9be    PENDING            3   adam            relu                                   32                 64                  3                 0          4.14473e-05         29 │
│ trial_3d9be    PENDING            2   rmsprop         tanh                                   32                 64                  5                 0          9.17858e-06         22 │
│ trial_3d9be    PENDING            3   adam            tanh                                   32                 16                  3                 0          1.1283e-05          20 │
│ trial_3d9be    PENDING            2   adam            relu                                   16                 64                  3                 0          3.03615e-05         15 │
│ trial_3d9be    PENDING            3   adam            tanh                                   16                 16                  3                 0          5.25384e-06         15 │
│ trial_3d9be    PENDING            2   adam            tanh                                   32                 16                  5                 0          9.96166e-05         17 │
│ trial_3d9be    PENDING            2   rmsprop         tanh                                   16                 32                  5                 1          9.49597e-06         15 │
│ trial_3d9be    PENDING            3   adam            tanh                                   16                 16                  3                 0          9.81418e-05         21 │
│ trial_3d9be    PENDING            2   adam            relu                                   16                 64                  5                 1          0.000124913         26 │
│ trial_3d9be    PENDING            2   adam            relu                                   16                 32                  3                 1          6.24019e-06         15 │
│ trial_3d9be    PENDING            2   adam            tanh                                   16                 64                  3                 1          8.38585e-05         24 │
│ trial_3d9be    PENDING            2   rmsprop         tanh                                   32                 32                  5                 1          0.000132925         20 │
│ trial_3d9be    PENDING            3   rmsprop         relu                                   32                 16                  5                 1          1.11714e-05         18 │
│ trial_3d9be    PENDING            3   adam            relu                                   16                 16                  5                 1          2.04657e-05         20 │
│ trial_3d9be    PENDING            3   adam            relu                                   32                 64                  3                 1          4.31547e-05         19 │
│ trial_3d9be    PENDING            3   rmsprop         tanh                                   32                 16                  5                 0          5.99499e-06         22 │
│ trial_3d9be    PENDING            2   adam            tanh                                   32                 16                  3                 0          4.35707e-05         17 │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

Trial trial_3d9be started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_3d9be config             │
├──────────────────────────────────────┤
│ N_capas                            3 │
│ epochs                            15 │
│ funcion_activacion              tanh │
│ num_resblocks                      0 │
│ numero_filtros                    16 │
│ optimizador                     adam │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  16 │
│ tasa_aprendizaje             0.00001 │
╰──────────────────────────────────────╯
Trial trial_3d9be started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_3d9be config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                            27 │
│ funcion_activacion              relu │
│ num_resblocks                      1 │
│ numero_filtros                    16 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00001 │
╰──────────────────────────────────────╯
Trial trial_3d9be started with configuration:
╭─────────────────────────────────────╮
│ Trial trial_3d9be config            │
├─────────────────────────────────────┤
│ N_capas                           3 │
│ epochs                           21 │
│ funcion_activacion             tanh │
│ num_resblocks                     0 │
│ numero_filtros                   16 │
│ optimizador                    adam │
│ tamanho_filtro                    3 │
│ tamanho_minilote                 16 │
│ tasa_aprendizaje             0.0001 │
╰─────────────────────────────────────╯
Trial trial_3d9be started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_3d9be config             │
├──────────────────────────────────────┤
│ N_capas                            3 │
│ epochs                            20 │
│ funcion_activacion              tanh │
│ num_resblocks                      0 │
│ numero_filtros                    16 │
│ optimizador                     adam │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00001 │
╰──────────────────────────────────────╯
Trial trial_3d9be started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_3d9be config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                            15 │
│ funcion_activacion              tanh │
│ num_resblocks                      1 │
│ numero_filtros                    32 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  16 │
│ tasa_aprendizaje             0.00001 │
╰──────────────────────────────────────╯
Trial trial_3d9be started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_3d9be config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                            25 │
│ funcion_activacion              tanh │
│ num_resblocks                      0 │
│ numero_filtros                    32 │
│ optimizador                     adam │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  16 │
│ tasa_aprendizaje             0.00001 │
╰──────────────────────────────────────╯
Trial trial_3d9be started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_3d9be config             │
├──────────────────────────────────────┤
│ N_capas                            3 │
│ epochs                            19 │
│ funcion_activacion              tanh │
│ num_resblocks                      0 │
│ numero_filtros                    16 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  16 │
│ tasa_aprendizaje             0.00009 │
╰──────────────────────────────────────╯
Trial trial_3d9be started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_3d9be config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                            22 │
│ funcion_activacion              tanh │
│ num_resblocks                      0 │
│ numero_filtros                    64 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00001 │
╰──────────────────────────────────────╯
Trial trial_3d9be started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_3d9be config             │
├──────────────────────────────────────┤
│ N_capas                            3 │
│ epochs                            19 │
│ funcion_activacion              relu │
│ num_resblocks                      1 │
│ numero_filtros                    64 │
│ optimizador                     adam │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00004 │
╰──────────────────────────────────────╯
Trial trial_3d9be started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_3d9be config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                            15 │
│ funcion_activacion              relu │
│ num_resblocks                      0 │
│ numero_filtros                    64 │
│ optimizador                     adam │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  16 │
│ tasa_aprendizaje             0.00003 │
╰──────────────────────────────────────╯
Trial trial_3d9be started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_3d9be config             │
├──────────────────────────────────────┤
│ N_capas                            3 │
│ epochs                            29 │
│ funcion_activacion              relu │
│ num_resblocks                      0 │
│ numero_filtros                    64 │
│ optimizador                     adam │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00004 │
╰──────────────────────────────────────╯
Trial trial_3d9be started with configuration:
[36m(train_cnn_ray_tune pid=1858837)[0m 2025-11-04 14:57:49.146355: 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=1858837)[0m 2025-11-04 14:57:49.168058: 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=1858837)[0m WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
[36m(train_cnn_ray_tune pid=1858837)[0m E0000 00:00:1762264669.197801 1859961 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=1858837)[0m E0000 00:00:1762264669.207338 1859961 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=1858837)[0m W0000 00:00:1762264669.227966 1859961 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=1858837)[0m W0000 00:00:1762264669.228018 1859961 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=1858837)[0m W0000 00:00:1762264669.228020 1859961 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=1858837)[0m W0000 00:00:1762264669.228022 1859961 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=1858837)[0m 2025-11-04 14:57:49.234111: 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=1858837)[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=1858837)[0m 2025-11-04 14:57:52.355581: 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=1858837)[0m 2025-11-04 14:57:52.355633: I external/local_xla/xla/stream_executor/cuda/cuda_diagnostics.cc:167] env: CUDA_VISIBLE_DEVICES=""
[36m(train_cnn_ray_tune pid=1858837)[0m 2025-11-04 14:57:52.355642: 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=1858837)[0m 2025-11-04 14:57:52.355646: 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=1858837)[0m 2025-11-04 14:57:52.355652: 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=1858837)[0m 2025-11-04 14:57:52.355655: I external/local_xla/xla/stream_executor/cuda/cuda_diagnostics.cc:190] hostname: simur-MS-7B94
[36m(train_cnn_ray_tune pid=1858837)[0m 2025-11-04 14:57:52.355858: 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=1858837)[0m 2025-11-04 14:57:52.355887: 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=1858837)[0m 2025-11-04 14:57:52.355891: I external/local_xla/xla/stream_executor/cuda/cuda_diagnostics.cc:291] kernel version seems to match DSO: 570.133.7
╭─────────────────────────────────────╮
│ Trial trial_3d9be config            │
├─────────────────────────────────────┤
│ N_capas                           2 │
│ epochs                           17 │
│ funcion_activacion             tanh │
│ num_resblocks                     0 │
│ numero_filtros                   16 │
│ optimizador                    adam │
│ tamanho_filtro                    5 │
│ tamanho_minilote                 32 │
│ tasa_aprendizaje             0.0001 │
╰─────────────────────────────────────╯
Trial trial_3d9be started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_3d9be config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                            20 │
│ funcion_activacion              tanh │
│ num_resblocks                      1 │
│ numero_filtros                    32 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00013 │
╰──────────────────────────────────────╯
Trial trial_3d9be started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_3d9be config             │
├──────────────────────────────────────┤
│ N_capas                            3 │
│ epochs                            18 │
│ funcion_activacion              relu │
│ num_resblocks                      1 │
│ numero_filtros                    16 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00001 │
╰──────────────────────────────────────╯
Trial trial_3d9be started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_3d9be config             │
├──────────────────────────────────────┤
│ N_capas                            3 │
│ epochs                            20 │
│ funcion_activacion              relu │
│ num_resblocks                      1 │
│ numero_filtros                    16 │
│ optimizador                     adam │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  16 │
│ tasa_aprendizaje             0.00002 │
╰──────────────────────────────────────╯
Trial trial_3d9be started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_3d9be config             │
├──────────────────────────────────────┤
│ N_capas                            3 │
│ epochs                            22 │
│ funcion_activacion              tanh │
│ num_resblocks                      0 │
│ numero_filtros                    16 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00001 │
╰──────────────────────────────────────╯
Trial trial_3d9be started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_3d9be config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                            17 │
│ funcion_activacion              tanh │
│ num_resblocks                      0 │
│ numero_filtros                    16 │
│ optimizador                     adam │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00004 │
╰──────────────────────────────────────╯
Trial trial_3d9be started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_3d9be config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                            24 │
│ funcion_activacion              tanh │
│ num_resblocks                      1 │
│ numero_filtros                    64 │
│ optimizador                     adam │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  16 │
│ tasa_aprendizaje             0.00008 │
╰──────────────────────────────────────╯
Trial trial_3d9be started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_3d9be config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                            26 │
│ funcion_activacion              relu │
│ num_resblocks                      1 │
│ numero_filtros                    64 │
│ optimizador                     adam │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  16 │
│ tasa_aprendizaje             0.00012 │
╰──────────────────────────────────────╯
Trial trial_3d9be started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_3d9be config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                            15 │
│ funcion_activacion              relu │
│ num_resblocks                      1 │
│ numero_filtros                    32 │
│ optimizador                     adam │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  16 │
│ tasa_aprendizaje             0.00001 │
╰──────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=1858837)[0m Epoch 1/15
[36m(train_cnn_ray_tune pid=1858840)[0m 
[1m  1/665[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m35:45[0m 3s/step - accuracy: 0.3125 - loss: 1.9830
[1m  4/665[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 19ms/step - accuracy: 0.3398 - loss: 1.7872
[36m(train_cnn_ray_tune pid=1858840)[0m 
[1m  8/665[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 17ms/step - accuracy: 0.3496 - loss: 1.7537
[1m 11/665[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 18ms/step - accuracy: 0.3451 - loss: 1.7745
[36m(train_cnn_ray_tune pid=1858840)[0m 
[1m 14/665[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 19ms/step - accuracy: 0.3410 - loss: 1.7865
[36m(train_cnn_ray_tune pid=1858840)[0m 
[1m 17/665[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 20ms/step - accuracy: 0.3358 - loss: 1.7983
[36m(train_cnn_ray_tune pid=1858840)[0m 
[1m 20/665[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 20ms/step - accuracy: 0.3308 - loss: 1.8126
[36m(train_cnn_ray_tune pid=1858840)[0m 
[1m 23/665[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 20ms/step - accuracy: 0.3260 - loss: 1.8263
[1m 26/665[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 20ms/step - accuracy: 0.3220 - loss: 1.8365
[36m(train_cnn_ray_tune pid=1858829)[0m 
[1m  5/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 28ms/step - accuracy: 0.2144 - loss: 1.9624
[1m  7/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 30ms/step - accuracy: 0.2313 - loss: 1.9249
[36m(train_cnn_ray_tune pid=1858840)[0m 
[1m 29/665[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 20ms/step - accuracy: 0.3181 - loss: 1.8467
[1m 32/665[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 20ms/step - accuracy: 0.3147 - loss: 1.8554
[36m(train_cnn_ray_tune pid=1858803)[0m 
[1m 11/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 24ms/step - accuracy: 0.1945 - loss: 2.0646
[36m(train_cnn_ray_tune pid=1858840)[0m 
[1m 35/665[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 20ms/step - accuracy: 0.3111 - loss: 1.8656
[36m(train_cnn_ray_tune pid=1858841)[0m 
[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m16:37[0m 3s/step - accuracy: 0.1562 - loss: 2.2389
[36m(train_cnn_ray_tune pid=1858840)[0m 
[1m 38/665[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 20ms/step - accuracy: 0.3075 - loss: 1.8765
[36m(train_cnn_ray_tune pid=1858840)[0m 
[1m 43/665[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 21ms/step - accuracy: 0.3027 - loss: 1.8925
[1m 45/665[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 22ms/step - accuracy: 0.3010 - loss: 1.8981
[36m(train_cnn_ray_tune pid=1858841)[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=1858837)[0m 
[1m  3/665[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m21s[0m 33ms/step - accuracy: 0.1840 - loss: 2.2211  
[1m  5/665[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m22s[0m 34ms/step - accuracy: 0.2167 - loss: 2.1071
[36m(train_cnn_ray_tune pid=1858838)[0m 
[1m  3/665[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m26s[0m 40ms/step - accuracy: 0.2847 - loss: 2.0607  
[36m(train_cnn_ray_tune pid=1858815)[0m 
[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m37:54[0m 7s/step - accuracy: 0.3125 - loss: 1.9539
[1m  2/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m22s[0m 69ms/step - accuracy: 0.3047 - loss: 1.8830[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=1858850)[0m 
[1m  6/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m22s[0m 67ms/step - accuracy: 0.2536 - loss: 2.0896
[1m  7/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m21s[0m 65ms/step - accuracy: 0.2556 - loss: 2.0771[32m [repeated 152x across cluster][0m
[36m(train_cnn_ray_tune pid=1858817)[0m 
[1m 21/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m17s[0m 57ms/step - accuracy: 0.2703 - loss: 1.9882[32m [repeated 218x across cluster][0m
[36m(train_cnn_ray_tune pid=1858836)[0m 
[1m 95/333[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m7s[0m 31ms/step - accuracy: 0.2581 - loss: 1.8559
[1m 97/333[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m7s[0m 31ms/step - accuracy: 0.2581 - loss: 1.8554[32m [repeated 101x across cluster][0m
[36m(train_cnn_ray_tune pid=1858841)[0m 
[1m144/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m6s[0m 34ms/step - accuracy: 0.2769 - loss: 1.8698[32m [repeated 67x across cluster][0m
[36m(train_cnn_ray_tune pid=1858850)[0m 
[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m39:32[0m 7s/step - accuracy: 0.1875 - loss: 2.0807[32m [repeated 11x across cluster][0m
[36m(train_cnn_ray_tune pid=1858842)[0m 
[1m 26/665[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m38s[0m 61ms/step - accuracy: 0.2467 - loss: 1.9884
[1m 27/665[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m38s[0m 60ms/step - accuracy: 0.2473 - loss: 1.9863
[1m 28/665[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m38s[0m 60ms/step - accuracy: 0.2480 - loss: 1.9841
[36m(train_cnn_ray_tune pid=1858836)[0m 
[1m127/333[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m6s[0m 31ms/step - accuracy: 0.2579 - loss: 1.8482
[1m129/333[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m6s[0m 31ms/step - accuracy: 0.2579 - loss: 1.8476
[1m131/333[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m6s[0m 31ms/step - accuracy: 0.2580 - loss: 1.8471
[36m(train_cnn_ray_tune pid=1858842)[0m 
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[36m(train_cnn_ray_tune pid=1858840)[0m 
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[36m(train_cnn_ray_tune pid=1858816)[0m 
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[36m(train_cnn_ray_tune pid=1858816)[0m 
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[36m(train_cnn_ray_tune pid=1858820)[0m 
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[36m(train_cnn_ray_tune pid=1858820)[0m 
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[36m(train_cnn_ray_tune pid=1858850)[0m 
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[36m(train_cnn_ray_tune pid=1858837)[0m 
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[36m(train_cnn_ray_tune pid=1858803)[0m 
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[36m(train_cnn_ray_tune pid=1858803)[0m Epoch 2/27
[36m(train_cnn_ray_tune pid=1858803)[0m 
[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m30s[0m 93ms/step - accuracy: 0.4062 - loss: 1.5042
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[36m(train_cnn_ray_tune pid=1858809)[0m 
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[36m(train_cnn_ray_tune pid=1858841)[0m 
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[36m(train_cnn_ray_tune pid=1858816)[0m 
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[36m(train_cnn_ray_tune pid=1858815)[0m 
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[36m(train_cnn_ray_tune pid=1858792)[0m 
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[36m(train_cnn_ray_tune pid=1858836)[0m 
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[36m(train_cnn_ray_tune pid=1858850)[0m 
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[36m(train_cnn_ray_tune pid=1858815)[0m 
[1m169/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m10s[0m 63ms/step - accuracy: 0.3359 - loss: 1.6658
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[36m(train_cnn_ray_tune pid=1858829)[0m 
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m19s[0m 47ms/step - accuracy: 0.2641 - loss: 1.9360 - val_accuracy: 0.3926 - val_loss: 1.4771[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=1858829)[0m Epoch 2/22[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=1858836)[0m 
[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m27s[0m 82ms/step - accuracy: 0.3438 - loss: 1.5980
[1m  3/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 29ms/step - accuracy: 0.3403 - loss: 1.5715 

Trial status: 20 RUNNING
Current time: 2025-11-04 14:58:16. 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_3d9be    RUNNING            3   rmsprop         tanh                                   16                 16                  3                 0          9.30537e-05         19 │
│ trial_3d9be    RUNNING            2   adam            tanh                                   16                 32                  5                 0          8.54171e-06         25 │
│ trial_3d9be    RUNNING            2   rmsprop         relu                                   32                 16                  5                 1          1.02234e-05         27 │
│ trial_3d9be    RUNNING            3   adam            relu                                   32                 64                  3                 0          4.14473e-05         29 │
│ trial_3d9be    RUNNING            2   rmsprop         tanh                                   32                 64                  5                 0          9.17858e-06         22 │
│ trial_3d9be    RUNNING            3   adam            tanh                                   32                 16                  3                 0          1.1283e-05          20 │
│ trial_3d9be    RUNNING            2   adam            relu                                   16                 64                  3                 0          3.03615e-05         15 │
│ trial_3d9be    RUNNING            3   adam            tanh                                   16                 16                  3                 0          5.25384e-06         15 │
│ trial_3d9be    RUNNING            2   adam            tanh                                   32                 16                  5                 0          9.96166e-05         17 │
│ trial_3d9be    RUNNING            2   rmsprop         tanh                                   16                 32                  5                 1          9.49597e-06         15 │
│ trial_3d9be    RUNNING            3   adam            tanh                                   16                 16                  3                 0          9.81418e-05         21 │
│ trial_3d9be    RUNNING            2   adam            relu                                   16                 64                  5                 1          0.000124913         26 │
│ trial_3d9be    RUNNING            2   adam            relu                                   16                 32                  3                 1          6.24019e-06         15 │
│ trial_3d9be    RUNNING            2   adam            tanh                                   16                 64                  3                 1          8.38585e-05         24 │
│ trial_3d9be    RUNNING            2   rmsprop         tanh                                   32                 32                  5                 1          0.000132925         20 │
│ trial_3d9be    RUNNING            3   rmsprop         relu                                   32                 16                  5                 1          1.11714e-05         18 │
│ trial_3d9be    RUNNING            3   adam            relu                                   16                 16                  5                 1          2.04657e-05         20 │
│ trial_3d9be    RUNNING            3   adam            relu                                   32                 64                  3                 1          4.31547e-05         19 │
│ trial_3d9be    RUNNING            3   rmsprop         tanh                                   32                 16                  5                 0          5.99499e-06         22 │
│ trial_3d9be    RUNNING            2   adam            tanh                                   32                 16                  3                 0          4.35707e-05         17 │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=1858844)[0m 
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[36m(train_cnn_ray_tune pid=1858842)[0m 
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[36m(train_cnn_ray_tune pid=1858838)[0m 
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[36m(train_cnn_ray_tune pid=1858844)[0m 
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[36m(train_cnn_ray_tune pid=1858820)[0m 
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[36m(train_cnn_ray_tune pid=1858850)[0m 
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[36m(train_cnn_ray_tune pid=1858816)[0m 
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[36m(train_cnn_ray_tune pid=1858817)[0m 
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[36m(train_cnn_ray_tune pid=1858817)[0m 
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[36m(train_cnn_ray_tune pid=1858817)[0m Epoch 2/20[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=1858840)[0m 
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[36m(train_cnn_ray_tune pid=1858840)[0m 
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[36m(train_cnn_ray_tune pid=1858817)[0m 
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[36m(train_cnn_ray_tune pid=1858817)[0m 
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[36m(train_cnn_ray_tune pid=1858820)[0m 
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[36m(train_cnn_ray_tune pid=1858844)[0m 
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[36m(train_cnn_ray_tune pid=1858809)[0m 
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[36m(train_cnn_ray_tune pid=1858815)[0m 
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[36m(train_cnn_ray_tune pid=1858841)[0m 
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[36m(train_cnn_ray_tune pid=1858841)[0m 
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m13s[0m 39ms/step - accuracy: 0.3857 - loss: 1.4421 - val_accuracy: 0.4333 - val_loss: 1.1373[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=1858841)[0m Epoch 3/20[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=1858829)[0m 
[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m33s[0m 101ms/step - accuracy: 0.3125 - loss: 2.0859[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=1858792)[0m 
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[36m(train_cnn_ray_tune pid=1858792)[0m 
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[36m(train_cnn_ray_tune pid=1858844)[0m 
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[36m(train_cnn_ray_tune pid=1858820)[0m 
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[36m(train_cnn_ray_tune pid=1858840)[0m 
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[36m(train_cnn_ray_tune pid=1858815)[0m 
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[36m(train_cnn_ray_tune pid=1858818)[0m 
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[36m(train_cnn_ray_tune pid=1858829)[0m 
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[36m(train_cnn_ray_tune pid=1858837)[0m 
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[36m(train_cnn_ray_tune pid=1858837)[0m Epoch 2/15[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=1858844)[0m 
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[36m(train_cnn_ray_tune pid=1858842)[0m 
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[36m(train_cnn_ray_tune pid=1858817)[0m 
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[36m(train_cnn_ray_tune pid=1858815)[0m 
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[36m(train_cnn_ray_tune pid=1858816)[0m 
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[36m(train_cnn_ray_tune pid=1858816)[0m 
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[36m(train_cnn_ray_tune pid=1858850)[0m 
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[36m(train_cnn_ray_tune pid=1858839)[0m 
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[36m(train_cnn_ray_tune pid=1858836)[0m 
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[36m(train_cnn_ray_tune pid=1858838)[0m 
[1m  1/665[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:00[0m 92ms/step - accuracy: 0.4375 - loss: 1.1676
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[36m(train_cnn_ray_tune pid=1858836)[0m 
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[36m(train_cnn_ray_tune pid=1858830)[0m 
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[36m(train_cnn_ray_tune pid=1858830)[0m Epoch 4/17[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=1858820)[0m 
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[36m(train_cnn_ray_tune pid=1858830)[0m 
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[36m(train_cnn_ray_tune pid=1858816)[0m 
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[36m(train_cnn_ray_tune pid=1858816)[0m 
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[36m(train_cnn_ray_tune pid=1858820)[0m 
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[36m(train_cnn_ray_tune pid=1858850)[0m 
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[36m(train_cnn_ray_tune pid=1858830)[0m 
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[36m(train_cnn_ray_tune pid=1858836)[0m 
[1m 63/333[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m9s[0m 36ms/step - accuracy: 0.3186 - loss: 1.5283 
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[36m(train_cnn_ray_tune pid=1858841)[0m 
[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m40s[0m 122ms/step - accuracy: 0.5000 - loss: 1.3108
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[36m(train_cnn_ray_tune pid=1858842)[0m 
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[36m(train_cnn_ray_tune pid=1858841)[0m 
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m12s[0m 36ms/step - accuracy: 0.3992 - loss: 1.3548 - val_accuracy: 0.4369 - val_loss: 1.1235
[36m(train_cnn_ray_tune pid=1858841)[0m Epoch 4/20
[36m(train_cnn_ray_tune pid=1858829)[0m 
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[36m(train_cnn_ray_tune pid=1858829)[0m Epoch 4/22
[36m(train_cnn_ray_tune pid=1858842)[0m 
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[36m(train_cnn_ray_tune pid=1858843)[0m 
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[36m(train_cnn_ray_tune pid=1858803)[0m 
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[36m(train_cnn_ray_tune pid=1858818)[0m 
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[36m(train_cnn_ray_tune pid=1858841)[0m 
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[36m(train_cnn_ray_tune pid=1858816)[0m 
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[36m(train_cnn_ray_tune pid=1858839)[0m 
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[36m(train_cnn_ray_tune pid=1858850)[0m 
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[36m(train_cnn_ray_tune pid=1858830)[0m 
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[36m(train_cnn_ray_tune pid=1858844)[0m 
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[36m(train_cnn_ray_tune pid=1858840)[0m 
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[36m(train_cnn_ray_tune pid=1858817)[0m 
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[36m(train_cnn_ray_tune pid=1858817)[0m 
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[36m(train_cnn_ray_tune pid=1858817)[0m Epoch 4/20[32m [repeated 10x across cluster][0m
Trial status: 20 RUNNING
Current time: 2025-11-04 14:58:46. 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_3d9be    RUNNING            3   rmsprop         tanh                                   16                 16                  3                 0          9.30537e-05         19 │
│ trial_3d9be    RUNNING            2   adam            tanh                                   16                 32                  5                 0          8.54171e-06         25 │
│ trial_3d9be    RUNNING            2   rmsprop         relu                                   32                 16                  5                 1          1.02234e-05         27 │
│ trial_3d9be    RUNNING            3   adam            relu                                   32                 64                  3                 0          4.14473e-05         29 │
│ trial_3d9be    RUNNING            2   rmsprop         tanh                                   32                 64                  5                 0          9.17858e-06         22 │
│ trial_3d9be    RUNNING            3   adam            tanh                                   32                 16                  3                 0          1.1283e-05          20 │
│ trial_3d9be    RUNNING            2   adam            relu                                   16                 64                  3                 0          3.03615e-05         15 │
│ trial_3d9be    RUNNING            3   adam            tanh                                   16                 16                  3                 0          5.25384e-06         15 │
│ trial_3d9be    RUNNING            2   adam            tanh                                   32                 16                  5                 0          9.96166e-05         17 │
│ trial_3d9be    RUNNING            2   rmsprop         tanh                                   16                 32                  5                 1          9.49597e-06         15 │
│ trial_3d9be    RUNNING            3   adam            tanh                                   16                 16                  3                 0          9.81418e-05         21 │
│ trial_3d9be    RUNNING            2   adam            relu                                   16                 64                  5                 1          0.000124913         26 │
│ trial_3d9be    RUNNING            2   adam            relu                                   16                 32                  3                 1          6.24019e-06         15 │
│ trial_3d9be    RUNNING            2   adam            tanh                                   16                 64                  3                 1          8.38585e-05         24 │
│ trial_3d9be    RUNNING            2   rmsprop         tanh                                   32                 32                  5                 1          0.000132925         20 │
│ trial_3d9be    RUNNING            3   rmsprop         relu                                   32                 16                  5                 1          1.11714e-05         18 │
│ trial_3d9be    RUNNING            3   adam            relu                                   16                 16                  5                 1          2.04657e-05         20 │
│ trial_3d9be    RUNNING            3   adam            relu                                   32                 64                  3                 1          4.31547e-05         19 │
│ trial_3d9be    RUNNING            3   rmsprop         tanh                                   32                 16                  5                 0          5.99499e-06         22 │
│ trial_3d9be    RUNNING            2   adam            tanh                                   32                 16                  3                 0          4.35707e-05         17 │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=1858850)[0m 
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[36m(train_cnn_ray_tune pid=1858820)[0m 
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[36m(train_cnn_ray_tune pid=1858820)[0m 
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[36m(train_cnn_ray_tune pid=1858830)[0m 
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[36m(train_cnn_ray_tune pid=1858842)[0m 
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[36m(train_cnn_ray_tune pid=1858836)[0m 
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[36m(train_cnn_ray_tune pid=1858815)[0m 
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[36m(train_cnn_ray_tune pid=1858850)[0m 
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[36m(train_cnn_ray_tune pid=1858844)[0m 
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[36m(train_cnn_ray_tune pid=1858836)[0m 
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[36m(train_cnn_ray_tune pid=1858829)[0m 
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[36m(train_cnn_ray_tune pid=1858816)[0m 
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[36m(train_cnn_ray_tune pid=1858816)[0m Epoch 3/15[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=1858818)[0m 
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[36m(train_cnn_ray_tune pid=1858816)[0m 
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[36m(train_cnn_ray_tune pid=1858816)[0m 
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[36m(train_cnn_ray_tune pid=1858820)[0m 
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[36m(train_cnn_ray_tune pid=1858844)[0m 
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[36m(train_cnn_ray_tune pid=1858836)[0m 
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[36m(train_cnn_ray_tune pid=1858840)[0m 
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[36m(train_cnn_ray_tune pid=1858809)[0m 
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[36m(train_cnn_ray_tune pid=1858838)[0m 
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[36m(train_cnn_ray_tune pid=1858837)[0m 
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[36m(train_cnn_ray_tune pid=1858837)[0m Epoch 3/15[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=1858792)[0m 
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[36m(train_cnn_ray_tune pid=1858816)[0m 
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[36m(train_cnn_ray_tune pid=1858843)[0m 
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[36m(train_cnn_ray_tune pid=1858841)[0m 
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[36m(train_cnn_ray_tune pid=1858820)[0m 
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[36m(train_cnn_ray_tune pid=1858839)[0m 
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[36m(train_cnn_ray_tune pid=1858838)[0m 
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[36m(train_cnn_ray_tune pid=1858836)[0m 
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[36m(train_cnn_ray_tune pid=1858850)[0m 
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[36m(train_cnn_ray_tune pid=1858850)[0m Epoch 4/19[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=1858829)[0m 
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[36m(train_cnn_ray_tune pid=1858820)[0m 
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[36m(train_cnn_ray_tune pid=1858818)[0m 
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[36m(train_cnn_ray_tune pid=1858844)[0m 
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[36m(train_cnn_ray_tune pid=1858816)[0m 
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[36m(train_cnn_ray_tune pid=1858829)[0m 
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[36m(train_cnn_ray_tune pid=1858809)[0m 
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[36m(train_cnn_ray_tune pid=1858815)[0m 
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[36m(train_cnn_ray_tune pid=1858840)[0m 
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[36m(train_cnn_ray_tune pid=1858840)[0m Epoch 4/15[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=1858836)[0m 
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[36m(train_cnn_ray_tune pid=1858839)[0m 
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[36m(train_cnn_ray_tune pid=1858842)[0m 
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[36m(train_cnn_ray_tune pid=1858818)[0m 
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[36m(train_cnn_ray_tune pid=1858792)[0m 
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[36m(train_cnn_ray_tune pid=1858816)[0m 
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[36m(train_cnn_ray_tune pid=1858820)[0m 
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[36m(train_cnn_ray_tune pid=1858841)[0m 
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[36m(train_cnn_ray_tune pid=1858841)[0m 
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[36m(train_cnn_ray_tune pid=1858837)[0m 
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[36m(train_cnn_ray_tune pid=1858841)[0m 
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[36m(train_cnn_ray_tune pid=1858841)[0m Epoch 6/20[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=1858839)[0m 
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[36m(train_cnn_ray_tune pid=1858842)[0m 
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[36m(train_cnn_ray_tune pid=1858830)[0m 
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[36m(train_cnn_ray_tune pid=1858820)[0m 
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[36m(train_cnn_ray_tune pid=1858837)[0m 
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[36m(train_cnn_ray_tune pid=1858836)[0m 
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[36m(train_cnn_ray_tune pid=1858817)[0m 
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[36m(train_cnn_ray_tune pid=1858836)[0m 
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[36m(train_cnn_ray_tune pid=1858829)[0m 
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m15s[0m 44ms/step - accuracy: 0.3683 - loss: 1.6490 - val_accuracy: 0.4442 - val_loss: 1.3706[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=1858829)[0m Epoch 6/22[32m [repeated 6x across cluster][0m
Trial status: 20 RUNNING
Current time: 2025-11-04 14:59:16. 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_3d9be    RUNNING            3   rmsprop         tanh                                   16                 16                  3                 0          9.30537e-05         19 │
│ trial_3d9be    RUNNING            2   adam            tanh                                   16                 32                  5                 0          8.54171e-06         25 │
│ trial_3d9be    RUNNING            2   rmsprop         relu                                   32                 16                  5                 1          1.02234e-05         27 │
│ trial_3d9be    RUNNING            3   adam            relu                                   32                 64                  3                 0          4.14473e-05         29 │
│ trial_3d9be    RUNNING            2   rmsprop         tanh                                   32                 64                  5                 0          9.17858e-06         22 │
│ trial_3d9be    RUNNING            3   adam            tanh                                   32                 16                  3                 0          1.1283e-05          20 │
│ trial_3d9be    RUNNING            2   adam            relu                                   16                 64                  3                 0          3.03615e-05         15 │
│ trial_3d9be    RUNNING            3   adam            tanh                                   16                 16                  3                 0          5.25384e-06         15 │
│ trial_3d9be    RUNNING            2   adam            tanh                                   32                 16                  5                 0          9.96166e-05         17 │
│ trial_3d9be    RUNNING            2   rmsprop         tanh                                   16                 32                  5                 1          9.49597e-06         15 │
│ trial_3d9be    RUNNING            3   adam            tanh                                   16                 16                  3                 0          9.81418e-05         21 │
│ trial_3d9be    RUNNING            2   adam            relu                                   16                 64                  5                 1          0.000124913         26 │
│ trial_3d9be    RUNNING            2   adam            relu                                   16                 32                  3                 1          6.24019e-06         15 │
│ trial_3d9be    RUNNING            2   adam            tanh                                   16                 64                  3                 1          8.38585e-05         24 │
│ trial_3d9be    RUNNING            2   rmsprop         tanh                                   32                 32                  5                 1          0.000132925         20 │
│ trial_3d9be    RUNNING            3   rmsprop         relu                                   32                 16                  5                 1          1.11714e-05         18 │
│ trial_3d9be    RUNNING            3   adam            relu                                   16                 16                  5                 1          2.04657e-05         20 │
│ trial_3d9be    RUNNING            3   adam            relu                                   32                 64                  3                 1          4.31547e-05         19 │
│ trial_3d9be    RUNNING            3   rmsprop         tanh                                   32                 16                  5                 0          5.99499e-06         22 │
│ trial_3d9be    RUNNING            2   adam            tanh                                   32                 16                  3                 0          4.35707e-05         17 │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=1858850)[0m 
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[36m(train_cnn_ray_tune pid=1858816)[0m 
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[36m(train_cnn_ray_tune pid=1858809)[0m 
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[36m(train_cnn_ray_tune pid=1858844)[0m 
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[36m(train_cnn_ray_tune pid=1858850)[0m 
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[36m(train_cnn_ray_tune pid=1858841)[0m 
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[36m(train_cnn_ray_tune pid=1858792)[0m 
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[36m(train_cnn_ray_tune pid=1858809)[0m 
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[36m(train_cnn_ray_tune pid=1858850)[0m 
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[36m(train_cnn_ray_tune pid=1858850)[0m 
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[36m(train_cnn_ray_tune pid=1858843)[0m 
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[36m(train_cnn_ray_tune pid=1858843)[0m Epoch 4/26[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=1858850)[0m 
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[36m(train_cnn_ray_tune pid=1858816)[0m 
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[36m(train_cnn_ray_tune pid=1858816)[0m 
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[36m(train_cnn_ray_tune pid=1858836)[0m 
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[36m(train_cnn_ray_tune pid=1858820)[0m 
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[36m(train_cnn_ray_tune pid=1858840)[0m 
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[36m(train_cnn_ray_tune pid=1858838)[0m 
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[36m(train_cnn_ray_tune pid=1858850)[0m 
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[36m(train_cnn_ray_tune pid=1858841)[0m 
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[36m(train_cnn_ray_tune pid=1858850)[0m 
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[36m(train_cnn_ray_tune pid=1858815)[0m 
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[36m(train_cnn_ray_tune pid=1858836)[0m 
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[36m(train_cnn_ray_tune pid=1858803)[0m 
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[36m(train_cnn_ray_tune pid=1858803)[0m 
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[36m(train_cnn_ray_tune pid=1858803)[0m Epoch 8/27[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=1858803)[0m 
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[36m(train_cnn_ray_tune pid=1858850)[0m 
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[36m(train_cnn_ray_tune pid=1858850)[0m 
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[36m(train_cnn_ray_tune pid=1858816)[0m 
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[36m(train_cnn_ray_tune pid=1858830)[0m 
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[36m(train_cnn_ray_tune pid=1858850)[0m 
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[36m(train_cnn_ray_tune pid=1858816)[0m 
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[36m(train_cnn_ray_tune pid=1858817)[0m 
[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m27s[0m 83ms/step - accuracy: 0.3438 - loss: 1.6723[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=1858830)[0m 
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[36m(train_cnn_ray_tune pid=1858820)[0m 
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[36m(train_cnn_ray_tune pid=1858809)[0m 
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[36m(train_cnn_ray_tune pid=1858829)[0m 
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[36m(train_cnn_ray_tune pid=1858844)[0m 
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[36m(train_cnn_ray_tune pid=1858809)[0m 
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[36m(train_cnn_ray_tune pid=1858809)[0m Epoch 5/25[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=1858817)[0m 
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[36m(train_cnn_ray_tune pid=1858842)[0m 
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[36m(train_cnn_ray_tune pid=1858838)[0m 
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[36m(train_cnn_ray_tune pid=1858816)[0m 
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[36m(train_cnn_ray_tune pid=1858850)[0m 
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[36m(train_cnn_ray_tune pid=1858820)[0m 
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[36m(train_cnn_ray_tune pid=1858850)[0m 
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[36m(train_cnn_ray_tune pid=1858841)[0m 
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[36m(train_cnn_ray_tune pid=1858836)[0m 
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[36m(train_cnn_ray_tune pid=1858836)[0m 
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[36m(train_cnn_ray_tune pid=1858836)[0m Epoch 9/17[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=1858820)[0m 
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[36m(train_cnn_ray_tune pid=1858839)[0m 
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[36m(train_cnn_ray_tune pid=1858820)[0m 
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[36m(train_cnn_ray_tune pid=1858792)[0m 
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[36m(train_cnn_ray_tune pid=1858803)[0m 
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[36m(train_cnn_ray_tune pid=1858850)[0m 
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[36m(train_cnn_ray_tune pid=1858850)[0m 
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[36m(train_cnn_ray_tune pid=1858838)[0m 
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[36m(train_cnn_ray_tune pid=1858816)[0m 
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[36m(train_cnn_ray_tune pid=1858850)[0m 
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[36m(train_cnn_ray_tune pid=1858816)[0m 
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[36m(train_cnn_ray_tune pid=1858816)[0m Epoch 5/15[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=1858844)[0m 
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[36m(train_cnn_ray_tune pid=1858850)[0m 
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[36m(train_cnn_ray_tune pid=1858816)[0m 
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[36m(train_cnn_ray_tune pid=1858836)[0m 
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[36m(train_cnn_ray_tune pid=1858830)[0m 
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[36m(train_cnn_ray_tune pid=1858815)[0m 
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[36m(train_cnn_ray_tune pid=1858844)[0m 
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[36m(train_cnn_ray_tune pid=1858844)[0m 
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[36m(train_cnn_ray_tune pid=1858842)[0m 
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[36m(train_cnn_ray_tune pid=1858829)[0m 
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[36m(train_cnn_ray_tune pid=1858841)[0m 
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[36m(train_cnn_ray_tune pid=1858844)[0m 
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[36m(train_cnn_ray_tune pid=1858792)[0m 
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[36m(train_cnn_ray_tune pid=1858792)[0m 
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[36m(train_cnn_ray_tune pid=1858792)[0m Epoch 5/19[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=1858850)[0m 
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Trial status: 20 RUNNING
Current time: 2025-11-04 14:59:46. 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_3d9be    RUNNING            3   rmsprop         tanh                                   16                 16                  3                 0          9.30537e-05         19 │
│ trial_3d9be    RUNNING            2   adam            tanh                                   16                 32                  5                 0          8.54171e-06         25 │
│ trial_3d9be    RUNNING            2   rmsprop         relu                                   32                 16                  5                 1          1.02234e-05         27 │
│ trial_3d9be    RUNNING            3   adam            relu                                   32                 64                  3                 0          4.14473e-05         29 │
│ trial_3d9be    RUNNING            2   rmsprop         tanh                                   32                 64                  5                 0          9.17858e-06         22 │
│ trial_3d9be    RUNNING            3   adam            tanh                                   32                 16                  3                 0          1.1283e-05          20 │
│ trial_3d9be    RUNNING            2   adam            relu                                   16                 64                  3                 0          3.03615e-05         15 │
│ trial_3d9be    RUNNING            3   adam            tanh                                   16                 16                  3                 0          5.25384e-06         15 │
│ trial_3d9be    RUNNING            2   adam            tanh                                   32                 16                  5                 0          9.96166e-05         17 │
│ trial_3d9be    RUNNING            2   rmsprop         tanh                                   16                 32                  5                 1          9.49597e-06         15 │
│ trial_3d9be    RUNNING            3   adam            tanh                                   16                 16                  3                 0          9.81418e-05         21 │
│ trial_3d9be    RUNNING            2   adam            relu                                   16                 64                  5                 1          0.000124913         26 │
│ trial_3d9be    RUNNING            2   adam            relu                                   16                 32                  3                 1          6.24019e-06         15 │
│ trial_3d9be    RUNNING            2   adam            tanh                                   16                 64                  3                 1          8.38585e-05         24 │
│ trial_3d9be    RUNNING            2   rmsprop         tanh                                   32                 32                  5                 1          0.000132925         20 │
│ trial_3d9be    RUNNING            3   rmsprop         relu                                   32                 16                  5                 1          1.11714e-05         18 │
│ trial_3d9be    RUNNING            3   adam            relu                                   16                 16                  5                 1          2.04657e-05         20 │
│ trial_3d9be    RUNNING            3   adam            relu                                   32                 64                  3                 1          4.31547e-05         19 │
│ trial_3d9be    RUNNING            3   rmsprop         tanh                                   32                 16                  5                 0          5.99499e-06         22 │
│ trial_3d9be    RUNNING            2   adam            tanh                                   32                 16                  3                 0          4.35707e-05         17 │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=1858820)[0m 
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[36m(train_cnn_ray_tune pid=1858816)[0m 
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[36m(train_cnn_ray_tune pid=1858816)[0m 
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[36m(train_cnn_ray_tune pid=1858850)[0m 
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[36m(train_cnn_ray_tune pid=1858842)[0m 
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[36m(train_cnn_ray_tune pid=1858843)[0m 
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[36m(train_cnn_ray_tune pid=1858830)[0m 
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[36m(train_cnn_ray_tune pid=1858840)[0m 
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[36m(train_cnn_ray_tune pid=1858840)[0m 
[1m  1/665[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:14[0m 112ms/step - accuracy: 0.2500 - loss: 1.5351[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=1858838)[0m 
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[36m(train_cnn_ray_tune pid=1858803)[0m 
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[36m(train_cnn_ray_tune pid=1858803)[0m 
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[36m(train_cnn_ray_tune pid=1858839)[0m 
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[36m(train_cnn_ray_tune pid=1858839)[0m Epoch 6/15[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=1858829)[0m 
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[36m(train_cnn_ray_tune pid=1858816)[0m 
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[36m(train_cnn_ray_tune pid=1858816)[0m 
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[36m(train_cnn_ray_tune pid=1858816)[0m 
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[36m(train_cnn_ray_tune pid=1858829)[0m 
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[36m(train_cnn_ray_tune pid=1858809)[0m 
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[36m(train_cnn_ray_tune pid=1858839)[0m 
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[36m(train_cnn_ray_tune pid=1858809)[0m 
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[36m(train_cnn_ray_tune pid=1858842)[0m 
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[36m(train_cnn_ray_tune pid=1858842)[0m 
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[36m(train_cnn_ray_tune pid=1858842)[0m Epoch 5/20[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1858839)[0m 
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[36m(train_cnn_ray_tune pid=1858809)[0m 
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[36m(train_cnn_ray_tune pid=1858818)[0m 
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[36m(train_cnn_ray_tune pid=1858836)[0m 
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[36m(train_cnn_ray_tune pid=1858816)[0m 
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[36m(train_cnn_ray_tune pid=1858837)[0m 
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[36m(train_cnn_ray_tune pid=1858803)[0m 
[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m27s[0m 84ms/step - accuracy: 0.5938 - loss: 1.0008[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=1858850)[0m 
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[36m(train_cnn_ray_tune pid=1858844)[0m 
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[36m(train_cnn_ray_tune pid=1858836)[0m 
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[36m(train_cnn_ray_tune pid=1858850)[0m 
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m20s[0m 61ms/step - accuracy: 0.4903 - loss: 1.2309 - val_accuracy: 0.5128 - val_loss: 1.1471[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=1858850)[0m Epoch 7/19[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=1858850)[0m 
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[36m(train_cnn_ray_tune pid=1858838)[0m 
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[36m(train_cnn_ray_tune pid=1858843)[0m 
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[36m(train_cnn_ray_tune pid=1858830)[0m 
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[36m(train_cnn_ray_tune pid=1858841)[0m 
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[36m(train_cnn_ray_tune pid=1858820)[0m 
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[36m(train_cnn_ray_tune pid=1858815)[0m 
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[36m(train_cnn_ray_tune pid=1858816)[0m 
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[36m(train_cnn_ray_tune pid=1858815)[0m 
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[36m(train_cnn_ray_tune pid=1858816)[0m 
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[36m(train_cnn_ray_tune pid=1858816)[0m Epoch 6/15[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=1858842)[0m 
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[36m(train_cnn_ray_tune pid=1858820)[0m 
[1m 74/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m11s[0m 43ms/step - accuracy: 0.3173 - loss: 1.7783[32m [repeated 142x across cluster][0m
[36m(train_cnn_ray_tune pid=1858836)[0m 
[1m  3/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 27ms/step - accuracy: 0.4375 - loss: 1.2358 
[1m  5/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 26ms/step - accuracy: 0.4353 - loss: 1.2299
[36m(train_cnn_ray_tune pid=1858850)[0m 
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[36m(train_cnn_ray_tune pid=1858841)[0m 
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[36m(train_cnn_ray_tune pid=1858818)[0m 
[1m  1/665[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m46s[0m 71ms/step - accuracy: 0.3750 - loss: 1.2619[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=1858803)[0m 
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[1m  5/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 34ms/step - accuracy: 0.3548 - loss: 1.5534
[36m(train_cnn_ray_tune pid=1858841)[0m 
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m12s[0m 37ms/step - accuracy: 0.4741 - loss: 1.1248 - val_accuracy: 0.4642 - val_loss: 1.1089[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=1858841)[0m Epoch 11/20[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=1858818)[0m 
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[36m(train_cnn_ray_tune pid=1858816)[0m 
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[36m(train_cnn_ray_tune pid=1858830)[0m 
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[1m 10/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 29ms/step - accuracy: 0.4193 - loss: 1.2708[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=1858792)[0m 
[1m  1/665[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m49s[0m 75ms/step - accuracy: 0.1250 - loss: 1.6294
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[36m(train_cnn_ray_tune pid=1858820)[0m 
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[36m(train_cnn_ray_tune pid=1858850)[0m 
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[36m(train_cnn_ray_tune pid=1858840)[0m 
[1m  3/665[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m30s[0m 46ms/step - accuracy: 0.3160 - loss: 2.0064  
[36m(train_cnn_ray_tune pid=1858840)[0m 
[1m  1/665[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:08[0m 103ms/step - accuracy: 0.3125 - loss: 1.8937[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=1858817)[0m 
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[1m  4/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 40ms/step - accuracy: 0.3366 - loss: 1.5407[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1858809)[0m 
[1m665/665[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m23s[0m 34ms/step - accuracy: 0.3459 - loss: 1.6110 - val_accuracy: 0.4185 - val_loss: 1.2484[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=1858809)[0m Epoch 7/25[32m [repeated 7x across cluster][0m
Trial status: 20 RUNNING
Current time: 2025-11-04 15:00:16. 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_3d9be    RUNNING            3   rmsprop         tanh                                   16                 16                  3                 0          9.30537e-05         19 │
│ trial_3d9be    RUNNING            2   adam            tanh                                   16                 32                  5                 0          8.54171e-06         25 │
│ trial_3d9be    RUNNING            2   rmsprop         relu                                   32                 16                  5                 1          1.02234e-05         27 │
│ trial_3d9be    RUNNING            3   adam            relu                                   32                 64                  3                 0          4.14473e-05         29 │
│ trial_3d9be    RUNNING            2   rmsprop         tanh                                   32                 64                  5                 0          9.17858e-06         22 │
│ trial_3d9be    RUNNING            3   adam            tanh                                   32                 16                  3                 0          1.1283e-05          20 │
│ trial_3d9be    RUNNING            2   adam            relu                                   16                 64                  3                 0          3.03615e-05         15 │
│ trial_3d9be    RUNNING            3   adam            tanh                                   16                 16                  3                 0          5.25384e-06         15 │
│ trial_3d9be    RUNNING            2   adam            tanh                                   32                 16                  5                 0          9.96166e-05         17 │
│ trial_3d9be    RUNNING            2   rmsprop         tanh                                   16                 32                  5                 1          9.49597e-06         15 │
│ trial_3d9be    RUNNING            3   adam            tanh                                   16                 16                  3                 0          9.81418e-05         21 │
│ trial_3d9be    RUNNING            2   adam            relu                                   16                 64                  5                 1          0.000124913         26 │
│ trial_3d9be    RUNNING            2   adam            relu                                   16                 32                  3                 1          6.24019e-06         15 │
│ trial_3d9be    RUNNING            2   adam            tanh                                   16                 64                  3                 1          8.38585e-05         24 │
│ trial_3d9be    RUNNING            2   rmsprop         tanh                                   32                 32                  5                 1          0.000132925         20 │
│ trial_3d9be    RUNNING            3   rmsprop         relu                                   32                 16                  5                 1          1.11714e-05         18 │
│ trial_3d9be    RUNNING            3   adam            relu                                   16                 16                  5                 1          2.04657e-05         20 │
│ trial_3d9be    RUNNING            3   adam            relu                                   32                 64                  3                 1          4.31547e-05         19 │
│ trial_3d9be    RUNNING            3   rmsprop         tanh                                   32                 16                  5                 0          5.99499e-06         22 │
│ trial_3d9be    RUNNING            2   adam            tanh                                   32                 16                  3                 0          4.35707e-05         17 │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=1858818)[0m 
[1m216/665[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m15s[0m 34ms/step - accuracy: 0.4463 - loss: 1.1823
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[36m(train_cnn_ray_tune pid=1858816)[0m 
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[36m(train_cnn_ray_tune pid=1858839)[0m 
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[36m(train_cnn_ray_tune pid=1858837)[0m 
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[36m(train_cnn_ray_tune pid=1858839)[0m 
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[36m(train_cnn_ray_tune pid=1858844)[0m 
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[36m(train_cnn_ray_tune pid=1858815)[0m 
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[36m(train_cnn_ray_tune pid=1858837)[0m 
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[36m(train_cnn_ray_tune pid=1858843)[0m 
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[36m(train_cnn_ray_tune pid=1858850)[0m 
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[36m(train_cnn_ray_tune pid=1858850)[0m 
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[36m(train_cnn_ray_tune pid=1858820)[0m 
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[36m(train_cnn_ray_tune pid=1858820)[0m Epoch 10/22[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=1858820)[0m 
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[36m(train_cnn_ray_tune pid=1858843)[0m 
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[36m(train_cnn_ray_tune pid=1858850)[0m 
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[36m(train_cnn_ray_tune pid=1858816)[0m 
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[36m(train_cnn_ray_tune pid=1858816)[0m 
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[36m(train_cnn_ray_tune pid=1858836)[0m 
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[36m(train_cnn_ray_tune pid=1858850)[0m 
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[36m(train_cnn_ray_tune pid=1858844)[0m 
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[36m(train_cnn_ray_tune pid=1858809)[0m 
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[36m(train_cnn_ray_tune pid=1858815)[0m 
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[36m(train_cnn_ray_tune pid=1858815)[0m 
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[36m(train_cnn_ray_tune pid=1858815)[0m Epoch 8/29[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=1858843)[0m 
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[36m(train_cnn_ray_tune pid=1858850)[0m 
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[36m(train_cnn_ray_tune pid=1858830)[0m 
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[36m(train_cnn_ray_tune pid=1858840)[0m 
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[36m(train_cnn_ray_tune pid=1858850)[0m 
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[36m(train_cnn_ray_tune pid=1858817)[0m 
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[36m(train_cnn_ray_tune pid=1858817)[0m 
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[36m(train_cnn_ray_tune pid=1858837)[0m 
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[36m(train_cnn_ray_tune pid=1858816)[0m 
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[36m(train_cnn_ray_tune pid=1858816)[0m Epoch 7/15[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=1858816)[0m 
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[36m(train_cnn_ray_tune pid=1858815)[0m 
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[36m(train_cnn_ray_tune pid=1858838)[0m 
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[36m(train_cnn_ray_tune pid=1858842)[0m 
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[36m(train_cnn_ray_tune pid=1858844)[0m 
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[36m(train_cnn_ray_tune pid=1858820)[0m 
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[36m(train_cnn_ray_tune pid=1858838)[0m 
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[36m(train_cnn_ray_tune pid=1858830)[0m 
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[36m(train_cnn_ray_tune pid=1858844)[0m 
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[36m(train_cnn_ray_tune pid=1858829)[0m 
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[36m(train_cnn_ray_tune pid=1858829)[0m 
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[36m(train_cnn_ray_tune pid=1858839)[0m 
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[36m(train_cnn_ray_tune pid=1858820)[0m 
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[36m(train_cnn_ray_tune pid=1858820)[0m Epoch 11/22[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=1858841)[0m 
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[36m(train_cnn_ray_tune pid=1858816)[0m 
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[36m(train_cnn_ray_tune pid=1858803)[0m 
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[36m(train_cnn_ray_tune pid=1858820)[0m 
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[36m(train_cnn_ray_tune pid=1858839)[0m 
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[36m(train_cnn_ray_tune pid=1858815)[0m 
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[36m(train_cnn_ray_tune pid=1858844)[0m 
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[36m(train_cnn_ray_tune pid=1858836)[0m 
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[36m(train_cnn_ray_tune pid=1858840)[0m 
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[36m(train_cnn_ray_tune pid=1858792)[0m 
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[36m(train_cnn_ray_tune pid=1858792)[0m 
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[36m(train_cnn_ray_tune pid=1858792)[0m Epoch 7/19[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=1858818)[0m 
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[36m(train_cnn_ray_tune pid=1858850)[0m 
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[36m(train_cnn_ray_tune pid=1858841)[0m 
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[36m(train_cnn_ray_tune pid=1858842)[0m 
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[36m(train_cnn_ray_tune pid=1858829)[0m 
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[36m(train_cnn_ray_tune pid=1858817)[0m 
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[36m(train_cnn_ray_tune pid=1858844)[0m 
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[36m(train_cnn_ray_tune pid=1858843)[0m 
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[36m(train_cnn_ray_tune pid=1858844)[0m 
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[36m(train_cnn_ray_tune pid=1858815)[0m 
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[36m(train_cnn_ray_tune pid=1858844)[0m 
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Trial status: 20 RUNNING
Current time: 2025-11-04 15:00:46. 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_3d9be    RUNNING            3   rmsprop         tanh                                   16                 16                  3                 0          9.30537e-05         19 │
│ trial_3d9be    RUNNING            2   adam            tanh                                   16                 32                  5                 0          8.54171e-06         25 │
│ trial_3d9be    RUNNING            2   rmsprop         relu                                   32                 16                  5                 1          1.02234e-05         27 │
│ trial_3d9be    RUNNING            3   adam            relu                                   32                 64                  3                 0          4.14473e-05         29 │
│ trial_3d9be    RUNNING            2   rmsprop         tanh                                   32                 64                  5                 0          9.17858e-06         22 │
│ trial_3d9be    RUNNING            3   adam            tanh                                   32                 16                  3                 0          1.1283e-05          20 │
│ trial_3d9be    RUNNING            2   adam            relu                                   16                 64                  3                 0          3.03615e-05         15 │
│ trial_3d9be    RUNNING            3   adam            tanh                                   16                 16                  3                 0          5.25384e-06         15 │
│ trial_3d9be    RUNNING            2   adam            tanh                                   32                 16                  5                 0          9.96166e-05         17 │
│ trial_3d9be    RUNNING            2   rmsprop         tanh                                   16                 32                  5                 1          9.49597e-06         15 │
│ trial_3d9be    RUNNING            3   adam            tanh                                   16                 16                  3                 0          9.81418e-05         21 │
│ trial_3d9be    RUNNING            2   adam            relu                                   16                 64                  5                 1          0.000124913         26 │
│ trial_3d9be    RUNNING            2   adam            relu                                   16                 32                  3                 1          6.24019e-06         15 │
│ trial_3d9be    RUNNING            2   adam            tanh                                   16                 64                  3                 1          8.38585e-05         24 │
│ trial_3d9be    RUNNING            2   rmsprop         tanh                                   32                 32                  5                 1          0.000132925         20 │
│ trial_3d9be    RUNNING            3   rmsprop         relu                                   32                 16                  5                 1          1.11714e-05         18 │
│ trial_3d9be    RUNNING            3   adam            relu                                   16                 16                  5                 1          2.04657e-05         20 │
│ trial_3d9be    RUNNING            3   adam            relu                                   32                 64                  3                 1          4.31547e-05         19 │
│ trial_3d9be    RUNNING            3   rmsprop         tanh                                   32                 16                  5                 0          5.99499e-06         22 │
│ trial_3d9be    RUNNING            2   adam            tanh                                   32                 16                  3                 0          4.35707e-05         17 │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=1858830)[0m 
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[36m(train_cnn_ray_tune pid=1858830)[0m Epoch 15/17[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=1858818)[0m 
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[36m(train_cnn_ray_tune pid=1858840)[0m 
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[36m(train_cnn_ray_tune pid=1858816)[0m 
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[36m(train_cnn_ray_tune pid=1858820)[0m 
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[36m(train_cnn_ray_tune pid=1858836)[0m 
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[36m(train_cnn_ray_tune pid=1858829)[0m 
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[36m(train_cnn_ray_tune pid=1858815)[0m 
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[36m(train_cnn_ray_tune pid=1858840)[0m 
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[36m(train_cnn_ray_tune pid=1858850)[0m 
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[36m(train_cnn_ray_tune pid=1858841)[0m 
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[36m(train_cnn_ray_tune pid=1858841)[0m 
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[36m(train_cnn_ray_tune pid=1858841)[0m 
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[36m(train_cnn_ray_tune pid=1858841)[0m 
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[36m(train_cnn_ray_tune pid=1858836)[0m Epoch 16/17[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1858841)[0m 
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[36m(train_cnn_ray_tune pid=1858841)[0m 
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[36m(train_cnn_ray_tune pid=1858841)[0m 
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[36m(train_cnn_ray_tune pid=1858841)[0m 
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[36m(train_cnn_ray_tune pid=1858841)[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=1858841)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=1858843)[0m 2025-11-04 14:57:49.667186: 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=1858843)[0m 2025-11-04 14:57:49.688746: 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=1858839)[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=1858839)[0m E0000 00:00:1762264669.756947 1860101 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=1858839)[0m E0000 00:00:1762264669.763731 1860101 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=1858843)[0m W0000 00:00:1762264669.745981 1860099 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=1858843)[0m 2025-11-04 14:57:49.752125: 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=1858843)[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=1858841)[0m 2025-11-04 14:57:53.050528: 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=1858841)[0m 2025-11-04 14:57:53.050644: 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=1858841)[0m 2025-11-04 14:57:53.050660: 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=1858841)[0m 2025-11-04 14:57:53.050668: 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=1858841)[0m 2025-11-04 14:57:53.050674: 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=1858841)[0m 2025-11-04 14:57:53.050691: 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=1858841)[0m 2025-11-04 14:57:53.051212: 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=1858841)[0m 2025-11-04 14:57:53.051281: 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=1858841)[0m 2025-11-04 14:57:53.051287: 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=1858841)[0m 
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[36m(train_cnn_ray_tune pid=1858841)[0m 
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[36m(train_cnn_ray_tune pid=1858841)[0m 
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[36m(train_cnn_ray_tune pid=1858843)[0m 
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[36m(train_cnn_ray_tune pid=1858841)[0m 
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[36m(train_cnn_ray_tune pid=1858820)[0m 
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[36m(train_cnn_ray_tune pid=1858841)[0m 
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[36m(train_cnn_ray_tune pid=1858841)[0m 
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[36m(train_cnn_ray_tune pid=1858838)[0m 
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[36m(train_cnn_ray_tune pid=1858841)[0m 
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Trial trial_3d9be finished iteration 1 at 2025-11-04 15:00:53. Total running time: 3min 7s
╭──────────────────────────────────────╮
│ Trial trial_3d9be result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             184.162 │
│ time_total_s                 184.162 │
│ training_iteration                 1 │
│ val_accuracy                  0.4793 │
╰──────────────────────────────────────╯

Trial trial_3d9be completed after 1 iterations at 2025-11-04 15:00:53. Total running time: 3min 7s
[36m(train_cnn_ray_tune pid=1858841)[0m 
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[36m(train_cnn_ray_tune pid=1858836)[0m 
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[36m(train_cnn_ray_tune pid=1858816)[0m 
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[36m(train_cnn_ray_tune pid=1858803)[0m 
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[36m(train_cnn_ray_tune pid=1858842)[0m 
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[36m(train_cnn_ray_tune pid=1858803)[0m 
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[36m(train_cnn_ray_tune pid=1858820)[0m 
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[36m(train_cnn_ray_tune pid=1858816)[0m 
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[36m(train_cnn_ray_tune pid=1858816)[0m Epoch 8/15[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=1858830)[0m 
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[36m(train_cnn_ray_tune pid=1858816)[0m 
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[36m(train_cnn_ray_tune pid=1858816)[0m 
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[36m(train_cnn_ray_tune pid=1858839)[0m 
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[36m(train_cnn_ray_tune pid=1858850)[0m 
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[36m(train_cnn_ray_tune pid=1858820)[0m 
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[36m(train_cnn_ray_tune pid=1858829)[0m 
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[36m(train_cnn_ray_tune pid=1858850)[0m 
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[36m(train_cnn_ray_tune pid=1858809)[0m 
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[36m(train_cnn_ray_tune pid=1858830)[0m 
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[36m(train_cnn_ray_tune pid=1858818)[0m 
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[36m(train_cnn_ray_tune pid=1858818)[0m Epoch 8/24[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=1858816)[0m 
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[36m(train_cnn_ray_tune pid=1858850)[0m 
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[36m(train_cnn_ray_tune pid=1858829)[0m 
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[36m(train_cnn_ray_tune pid=1858829)[0m 
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[36m(train_cnn_ray_tune pid=1858830)[0m 
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[36m(train_cnn_ray_tune pid=1858820)[0m 
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[36m(train_cnn_ray_tune pid=1858820)[0m 
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[36m(train_cnn_ray_tune pid=1858820)[0m Epoch 13/22[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=1858820)[0m 
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[36m(train_cnn_ray_tune pid=1858830)[0m 
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[36m(train_cnn_ray_tune pid=1858850)[0m 
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[36m(train_cnn_ray_tune pid=1858816)[0m 
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[36m(train_cnn_ray_tune pid=1858836)[0m 
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[36m(train_cnn_ray_tune pid=1858836)[0m 
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[36m(train_cnn_ray_tune pid=1858836)[0m 
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[36m(train_cnn_ray_tune pid=1858836)[0m 
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[36m(train_cnn_ray_tune pid=1858836)[0m 
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[36m(train_cnn_ray_tune pid=1858836)[0m 
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Trial trial_3d9be finished iteration 1 at 2025-11-04 15:01:11. Total running time: 3min 26s
[36m(train_cnn_ray_tune pid=1858836)[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=1858836)[0m   _log_deprecation_warning(
╭──────────────────────────────────────╮
│ Trial trial_3d9be result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             202.869 │
│ time_total_s                 202.869 │
│ training_iteration                 1 │
│ val_accuracy                 0.41393 │
╰──────────────────────────────────────╯

Trial trial_3d9be completed after 1 iterations at 2025-11-04 15:01:12. Total running time: 3min 26s
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[36m(train_cnn_ray_tune pid=1858817)[0m 
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[36m(train_cnn_ray_tune pid=1858817)[0m Epoch 14/20[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=1858817)[0m 
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[36m(train_cnn_ray_tune pid=1858817)[0m 
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[36m(train_cnn_ray_tune pid=1858792)[0m 
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[36m(train_cnn_ray_tune pid=1858820)[0m 
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[36m(train_cnn_ray_tune pid=1858838)[0m 
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[36m(train_cnn_ray_tune pid=1858837)[0m 
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[36m(train_cnn_ray_tune pid=1858816)[0m 
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[36m(train_cnn_ray_tune pid=1858850)[0m 
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[36m(train_cnn_ray_tune pid=1858844)[0m 
[1m 41/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 34ms/step - accuracy: 0.4222 - loss: 1.3484 
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[36m(train_cnn_ray_tune pid=1858803)[0m 
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Trial status: 18 RUNNING | 2 TERMINATED
Current time: 2025-11-04 15:01:16. Total running time: 3min 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_3d9be    RUNNING              3   rmsprop         tanh                                   16                 16                  3                 0          9.30537e-05         19                                              │
│ trial_3d9be    RUNNING              2   adam            tanh                                   16                 32                  5                 0          8.54171e-06         25                                              │
│ trial_3d9be    RUNNING              2   rmsprop         relu                                   32                 16                  5                 1          1.02234e-05         27                                              │
│ trial_3d9be    RUNNING              3   adam            relu                                   32                 64                  3                 0          4.14473e-05         29                                              │
│ trial_3d9be    RUNNING              2   rmsprop         tanh                                   32                 64                  5                 0          9.17858e-06         22                                              │
│ trial_3d9be    RUNNING              3   adam            tanh                                   32                 16                  3                 0          1.1283e-05          20                                              │
│ trial_3d9be    RUNNING              2   adam            relu                                   16                 64                  3                 0          3.03615e-05         15                                              │
│ trial_3d9be    RUNNING              3   adam            tanh                                   16                 16                  3                 0          5.25384e-06         15                                              │
│ trial_3d9be    RUNNING              2   adam            tanh                                   32                 16                  5                 0          9.96166e-05         17                                              │
│ trial_3d9be    RUNNING              2   rmsprop         tanh                                   16                 32                  5                 1          9.49597e-06         15                                              │
│ trial_3d9be    RUNNING              3   adam            tanh                                   16                 16                  3                 0          9.81418e-05         21                                              │
│ trial_3d9be    RUNNING              2   adam            relu                                   16                 64                  5                 1          0.000124913         26                                              │
│ trial_3d9be    RUNNING              2   adam            relu                                   16                 32                  3                 1          6.24019e-06         15                                              │
│ trial_3d9be    RUNNING              2   adam            tanh                                   16                 64                  3                 1          8.38585e-05         24                                              │
│ trial_3d9be    RUNNING              3   rmsprop         relu                                   32                 16                  5                 1          1.11714e-05         18                                              │
│ trial_3d9be    RUNNING              3   adam            relu                                   16                 16                  5                 1          2.04657e-05         20                                              │
│ trial_3d9be    RUNNING              3   adam            relu                                   32                 64                  3                 1          4.31547e-05         19                                              │
│ trial_3d9be    RUNNING              3   rmsprop         tanh                                   32                 16                  5                 0          5.99499e-06         22                                              │
│ trial_3d9be    TERMINATED           2   rmsprop         tanh                                   32                 32                  5                 1          0.000132925         20        1            184.162         0.479304 │
│ trial_3d9be    TERMINATED           2   adam            tanh                                   32                 16                  3                 0          4.35707e-05         17        1            202.869         0.413929 │
╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=1858830)[0m 
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[36m(train_cnn_ray_tune pid=1858830)[0m 
[1m 7/49[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 9ms/step   
[1m12/49[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 11ms/step
[36m(train_cnn_ray_tune pid=1858830)[0m 
[1m16/49[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 12ms/step
[1m20/49[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 12ms/step
[36m(train_cnn_ray_tune pid=1858850)[0m 
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m17s[0m 52ms/step - accuracy: 0.5490 - loss: 1.0855 - val_accuracy: 0.5256 - val_loss: 1.0671[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=1858850)[0m Epoch 11/19[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=1858829)[0m 
[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m23s[0m 72ms/step - accuracy: 0.5000 - loss: 1.2525
[1m  3/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 25ms/step - accuracy: 0.4444 - loss: 1.3343 [32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1858830)[0m 
[1m25/49[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 12ms/step
[1m30/49[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 12ms/step
[36m(train_cnn_ray_tune pid=1858850)[0m 
[1m  2/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m17s[0m 53ms/step - accuracy: 0.4844 - loss: 1.1360 
[1m  3/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m17s[0m 52ms/step - accuracy: 0.4722 - loss: 1.1530
[36m(train_cnn_ray_tune pid=1858830)[0m 
[1m34/49[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 12ms/step
[1m39/49[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 12ms/step
[36m(train_cnn_ray_tune pid=1858830)[0m 
[1m46/49[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 11ms/step
[36m(train_cnn_ray_tune pid=1858830)[0m 
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 16ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 17ms/step
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[36m(train_cnn_ray_tune pid=1858843)[0m 
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[36m(train_cnn_ray_tune pid=1858830)[0m 
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[36m(train_cnn_ray_tune pid=1858850)[0m 
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[36m(train_cnn_ray_tune pid=1858830)[0m 
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[36m(train_cnn_ray_tune pid=1858844)[0m 
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[36m(train_cnn_ray_tune pid=1858830)[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=1858830)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=1858818)[0m 
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[36m(train_cnn_ray_tune pid=1858817)[0m 
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Trial trial_3d9be finished iteration 1 at 2025-11-04 15:01:19. Total running time: 3min 33s
╭──────────────────────────────────────╮
│ Trial trial_3d9be result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             210.178 │
│ time_total_s                 210.178 │
│ training_iteration                 1 │
│ val_accuracy                 0.37878 │
╰──────────────────────────────────────╯

Trial trial_3d9be completed after 1 iterations at 2025-11-04 15:01:19. Total running time: 3min 33s
[36m(train_cnn_ray_tune pid=1858840)[0m 
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[36m(train_cnn_ray_tune pid=1858839)[0m 
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[36m(train_cnn_ray_tune pid=1858843)[0m 
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[36m(train_cnn_ray_tune pid=1858837)[0m 
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[36m(train_cnn_ray_tune pid=1858837)[0m Epoch 8/15[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=1858837)[0m 
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[36m(train_cnn_ray_tune pid=1858842)[0m 
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[36m(train_cnn_ray_tune pid=1858837)[0m 
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[36m(train_cnn_ray_tune pid=1858844)[0m 
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[36m(train_cnn_ray_tune pid=1858820)[0m 
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[36m(train_cnn_ray_tune pid=1858843)[0m 
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[36m(train_cnn_ray_tune pid=1858809)[0m 
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[36m(train_cnn_ray_tune pid=1858839)[0m 
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[36m(train_cnn_ray_tune pid=1858815)[0m 
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[36m(train_cnn_ray_tune pid=1858844)[0m 
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[36m(train_cnn_ray_tune pid=1858844)[0m Epoch 15/18[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=1858844)[0m 
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[36m(train_cnn_ray_tune pid=1858816)[0m 
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[36m(train_cnn_ray_tune pid=1858844)[0m 
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[36m(train_cnn_ray_tune pid=1858850)[0m 
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[36m(train_cnn_ray_tune pid=1858844)[0m 
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[36m(train_cnn_ray_tune pid=1858844)[0m 
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[36m(train_cnn_ray_tune pid=1858792)[0m 
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[36m(train_cnn_ray_tune pid=1858792)[0m 
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[36m(train_cnn_ray_tune pid=1858842)[0m 
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[36m(train_cnn_ray_tune pid=1858792)[0m 
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[36m(train_cnn_ray_tune pid=1858792)[0m Epoch 9/19[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1858818)[0m 
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[36m(train_cnn_ray_tune pid=1858837)[0m 
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[36m(train_cnn_ray_tune pid=1858816)[0m 
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[36m(train_cnn_ray_tune pid=1858844)[0m 
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[36m(train_cnn_ray_tune pid=1858809)[0m 
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[36m(train_cnn_ray_tune pid=1858803)[0m 
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[36m(train_cnn_ray_tune pid=1858843)[0m 
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[36m(train_cnn_ray_tune pid=1858817)[0m 
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[36m(train_cnn_ray_tune pid=1858817)[0m 
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[36m(train_cnn_ray_tune pid=1858820)[0m 
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[36m(train_cnn_ray_tune pid=1858817)[0m 
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[36m(train_cnn_ray_tune pid=1858817)[0m Epoch 16/20[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=1858792)[0m 
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[36m(train_cnn_ray_tune pid=1858839)[0m 
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[36m(train_cnn_ray_tune pid=1858792)[0m 
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[36m(train_cnn_ray_tune pid=1858817)[0m 
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[36m(train_cnn_ray_tune pid=1858829)[0m 
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[36m(train_cnn_ray_tune pid=1858809)[0m 
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[36m(train_cnn_ray_tune pid=1858829)[0m 
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[36m(train_cnn_ray_tune pid=1858842)[0m 
[1m665/665[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m24s[0m 36ms/step - accuracy: 0.4372 - loss: 1.3263 - val_accuracy: 0.4944 - val_loss: 1.1348[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=1858842)[0m Epoch 9/20[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=1858842)[0m 
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[36m(train_cnn_ray_tune pid=1858842)[0m 
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[36m(train_cnn_ray_tune pid=1858839)[0m 
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[36m(train_cnn_ray_tune pid=1858792)[0m 
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[36m(train_cnn_ray_tune pid=1858820)[0m 
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[36m(train_cnn_ray_tune pid=1858837)[0m 
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Trial status: 17 RUNNING | 3 TERMINATED
Current time: 2025-11-04 15:01:46. Total running time: 4min 0s
Logical resource usage: 17.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     num_resblocks     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_3d9be    RUNNING              3   rmsprop         tanh                                   16                 16                  3                 0          9.30537e-05         19                                              │
│ trial_3d9be    RUNNING              2   adam            tanh                                   16                 32                  5                 0          8.54171e-06         25                                              │
│ trial_3d9be    RUNNING              2   rmsprop         relu                                   32                 16                  5                 1          1.02234e-05         27                                              │
│ trial_3d9be    RUNNING              3   adam            relu                                   32                 64                  3                 0          4.14473e-05         29                                              │
│ trial_3d9be    RUNNING              2   rmsprop         tanh                                   32                 64                  5                 0          9.17858e-06         22                                              │
│ trial_3d9be    RUNNING              3   adam            tanh                                   32                 16                  3                 0          1.1283e-05          20                                              │
│ trial_3d9be    RUNNING              2   adam            relu                                   16                 64                  3                 0          3.03615e-05         15                                              │
│ trial_3d9be    RUNNING              3   adam            tanh                                   16                 16                  3                 0          5.25384e-06         15                                              │
│ trial_3d9be    RUNNING              2   rmsprop         tanh                                   16                 32                  5                 1          9.49597e-06         15                                              │
│ trial_3d9be    RUNNING              3   adam            tanh                                   16                 16                  3                 0          9.81418e-05         21                                              │
│ trial_3d9be    RUNNING              2   adam            relu                                   16                 64                  5                 1          0.000124913         26                                              │
│ trial_3d9be    RUNNING              2   adam            relu                                   16                 32                  3                 1          6.24019e-06         15                                              │
│ trial_3d9be    RUNNING              2   adam            tanh                                   16                 64                  3                 1          8.38585e-05         24                                              │
│ trial_3d9be    RUNNING              3   rmsprop         relu                                   32                 16                  5                 1          1.11714e-05         18                                              │
│ trial_3d9be    RUNNING              3   adam            relu                                   16                 16                  5                 1          2.04657e-05         20                                              │
│ trial_3d9be    RUNNING              3   adam            relu                                   32                 64                  3                 1          4.31547e-05         19                                              │
│ trial_3d9be    RUNNING              3   rmsprop         tanh                                   32                 16                  5                 0          5.99499e-06         22                                              │
│ trial_3d9be    TERMINATED           2   adam            tanh                                   32                 16                  5                 0          9.96166e-05         17        1            210.178         0.378778 │
│ trial_3d9be    TERMINATED           2   rmsprop         tanh                                   32                 32                  5                 1          0.000132925         20        1            184.162         0.479304 │
│ trial_3d9be    TERMINATED           2   adam            tanh                                   32                 16                  3                 0          4.35707e-05         17        1            202.869         0.413929 │
╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=1858818)[0m 
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[36m(train_cnn_ray_tune pid=1858803)[0m 
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[1m  6/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 24ms/step - accuracy: 0.4536 - loss: 1.3107[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=1858820)[0m 
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m13s[0m 38ms/step - accuracy: 0.3598 - loss: 1.5964 - val_accuracy: 0.4120 - val_loss: 1.4111[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=1858820)[0m Epoch 16/22[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=1858818)[0m 
[1m 72/665[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m16s[0m 28ms/step - accuracy: 0.5228 - loss: 1.0096
[1m 74/665[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m16s[0m 28ms/step - accuracy: 0.5228 - loss: 1.0101
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[36m(train_cnn_ray_tune pid=1858842)[0m 
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[36m(train_cnn_ray_tune pid=1858842)[0m 
[1m212/665[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m13s[0m 29ms/step - accuracy: 0.4365 - loss: 1.3153[32m [repeated 76x across cluster][0m
[36m(train_cnn_ray_tune pid=1858844)[0m 
[1m318/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 32ms/step - accuracy: 0.4271 - loss: 1.3492
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[36m(train_cnn_ray_tune pid=1858803)[0m 
[1m184/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.4250 - loss: 1.3121[32m [repeated 248x across cluster][0m
[36m(train_cnn_ray_tune pid=1858817)[0m 
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[36m(train_cnn_ray_tune pid=1858829)[0m 
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[36m(train_cnn_ray_tune pid=1858829)[0m 
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[1m  6/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 26ms/step - accuracy: 0.3759 - loss: 1.5181[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1858844)[0m 
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[36m(train_cnn_ray_tune pid=1858844)[0m Epoch 17/18[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=1858818)[0m 
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[1m247/665[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m11s[0m 26ms/step - accuracy: 0.5180 - loss: 1.0325
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[36m(train_cnn_ray_tune pid=1858792)[0m 
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[36m(train_cnn_ray_tune pid=1858792)[0m 
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[36m(train_cnn_ray_tune pid=1858850)[0m 
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[1m135/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m7s[0m 39ms/step - accuracy: 0.5825 - loss: 0.9927[32m [repeated 333x across cluster][0m
[36m(train_cnn_ray_tune pid=1858829)[0m 
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[36m(train_cnn_ray_tune pid=1858844)[0m 
[1m104/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m6s[0m 30ms/step - accuracy: 0.4404 - loss: 1.3186
[1m106/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m6s[0m 30ms/step - accuracy: 0.4403 - loss: 1.3190
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[36m(train_cnn_ray_tune pid=1858803)[0m 
[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m23s[0m 69ms/step - accuracy: 0.4062 - loss: 1.3240
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[36m(train_cnn_ray_tune pid=1858839)[0m 
[1m  1/665[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m41s[0m 62ms/step - accuracy: 0.5625 - loss: 1.1034[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=1858818)[0m 
[1m290/665[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m9s[0m 27ms/step - accuracy: 0.5177 - loss: 1.0352 
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[36m(train_cnn_ray_tune pid=1858839)[0m 
[1m665/665[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m19s[0m 28ms/step - accuracy: 0.3939 - loss: 1.5320 - val_accuracy: 0.4671 - val_loss: 1.2530[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=1858839)[0m Epoch 12/15[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=1858838)[0m 
[1m 98/665[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m16s[0m 28ms/step - accuracy: 0.4599 - loss: 1.1738
[1m100/665[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m16s[0m 28ms/step - accuracy: 0.4598 - loss: 1.1738[32m [repeated 77x across cluster][0m
[36m(train_cnn_ray_tune pid=1858839)[0m 
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[36m(train_cnn_ray_tune pid=1858820)[0m 
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[36m(train_cnn_ray_tune pid=1858844)[0m 
[1m255/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m2s[0m 31ms/step - accuracy: 0.4374 - loss: 1.3247
[1m256/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m2s[0m 31ms/step - accuracy: 0.4374 - loss: 1.3247[32m [repeated 254x across cluster][0m
[36m(train_cnn_ray_tune pid=1858844)[0m 
[1m258/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m2s[0m 31ms/step - accuracy: 0.4374 - loss: 1.3247[32m [repeated 199x across cluster][0m
[36m(train_cnn_ray_tune pid=1858840)[0m 
[1m  1/665[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m51s[0m 77ms/step - accuracy: 0.6250 - loss: 1.0824
[1m  4/665[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m16s[0m 25ms/step - accuracy: 0.5286 - loss: 1.2045[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1858820)[0m 
[1m 51/333[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m9s[0m 35ms/step - accuracy: 0.3472 - loss: 1.6550 
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[36m(train_cnn_ray_tune pid=1858816)[0m 
[1m  3/665[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m24s[0m 37ms/step - accuracy: 0.5069 - loss: 1.1785   
[36m(train_cnn_ray_tune pid=1858818)[0m 
[1m585/665[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m2s[0m 27ms/step - accuracy: 0.5163 - loss: 1.0463
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[1m589/665[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m2s[0m 27ms/step - accuracy: 0.5163 - loss: 1.0463
[36m(train_cnn_ray_tune pid=1858816)[0m 
[1m  1/665[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3:33:38[0m 19s/step - accuracy: 0.5625 - loss: 1.0795[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=1858817)[0m 
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m11s[0m 34ms/step - accuracy: 0.3955 - loss: 1.4326 - val_accuracy: 0.4290 - val_loss: 1.1554[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=1858817)[0m Epoch 18/20[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=1858843)[0m 
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[1m239/665[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m12s[0m 29ms/step - accuracy: 0.6459 - loss: 0.8083[32m [repeated 178x across cluster][0m
[36m(train_cnn_ray_tune pid=1858816)[0m 
[1m 95/665[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m15s[0m 26ms/step - accuracy: 0.4733 - loss: 1.2372[32m [repeated 101x across cluster][0m
[36m(train_cnn_ray_tune pid=1858809)[0m 
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[36m(train_cnn_ray_tune pid=1858803)[0m 
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[36m(train_cnn_ray_tune pid=1858792)[0m 
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[36m(train_cnn_ray_tune pid=1858844)[0m 
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[36m(train_cnn_ray_tune pid=1858839)[0m 
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[36m(train_cnn_ray_tune pid=1858837)[0m 
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[36m(train_cnn_ray_tune pid=1858839)[0m 
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[36m(train_cnn_ray_tune pid=1858818)[0m 
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[36m(train_cnn_ray_tune pid=1858818)[0m 
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[36m(train_cnn_ray_tune pid=1858818)[0m 
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[36m(train_cnn_ray_tune pid=1858818)[0m Epoch 11/24[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=1858816)[0m 
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[36m(train_cnn_ray_tune pid=1858816)[0m 
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[36m(train_cnn_ray_tune pid=1858850)[0m 
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[36m(train_cnn_ray_tune pid=1858840)[0m 
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[36m(train_cnn_ray_tune pid=1858850)[0m 
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[36m(train_cnn_ray_tune pid=1858829)[0m 
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[36m(train_cnn_ray_tune pid=1858820)[0m 
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[36m(train_cnn_ray_tune pid=1858837)[0m 
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[36m(train_cnn_ray_tune pid=1858820)[0m 
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m13s[0m 39ms/step - accuracy: 0.3577 - loss: 1.6060 - val_accuracy: 0.4133 - val_loss: 1.4012[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1858820)[0m Epoch 18/22[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1858803)[0m 
[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m26s[0m 79ms/step - accuracy: 0.4375 - loss: 1.4912
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[36m(train_cnn_ray_tune pid=1858842)[0m 
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[36m(train_cnn_ray_tune pid=1858818)[0m 
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[36m(train_cnn_ray_tune pid=1858820)[0m 
[1m 81/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m8s[0m 34ms/step - accuracy: 0.3660 - loss: 1.5631
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[36m(train_cnn_ray_tune pid=1858844)[0m 
[1m317/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 35ms/step - accuracy: 0.4414 - loss: 1.3325[32m [repeated 245x across cluster][0m
[36m(train_cnn_ray_tune pid=1858829)[0m 
[1m  9/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 30ms/step - accuracy: 0.4510 - loss: 1.2949 
[1m 11/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 31ms/step - accuracy: 0.4440 - loss: 1.3142
Trial status: 17 RUNNING | 3 TERMINATED
Current time: 2025-11-04 15:02:16. Total running time: 4min 30s
Logical resource usage: 17.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     num_resblocks     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_3d9be    RUNNING              3   rmsprop         tanh                                   16                 16                  3                 0          9.30537e-05         19                                              │
│ trial_3d9be    RUNNING              2   adam            tanh                                   16                 32                  5                 0          8.54171e-06         25                                              │
│ trial_3d9be    RUNNING              2   rmsprop         relu                                   32                 16                  5                 1          1.02234e-05         27                                              │
│ trial_3d9be    RUNNING              3   adam            relu                                   32                 64                  3                 0          4.14473e-05         29                                              │
│ trial_3d9be    RUNNING              2   rmsprop         tanh                                   32                 64                  5                 0          9.17858e-06         22                                              │
│ trial_3d9be    RUNNING              3   adam            tanh                                   32                 16                  3                 0          1.1283e-05          20                                              │
│ trial_3d9be    RUNNING              2   adam            relu                                   16                 64                  3                 0          3.03615e-05         15                                              │
│ trial_3d9be    RUNNING              3   adam            tanh                                   16                 16                  3                 0          5.25384e-06         15                                              │
│ trial_3d9be    RUNNING              2   rmsprop         tanh                                   16                 32                  5                 1          9.49597e-06         15                                              │
│ trial_3d9be    RUNNING              3   adam            tanh                                   16                 16                  3                 0          9.81418e-05         21                                              │
│ trial_3d9be    RUNNING              2   adam            relu                                   16                 64                  5                 1          0.000124913         26                                              │
│ trial_3d9be    RUNNING              2   adam            relu                                   16                 32                  3                 1          6.24019e-06         15                                              │
│ trial_3d9be    RUNNING              2   adam            tanh                                   16                 64                  3                 1          8.38585e-05         24                                              │
│ trial_3d9be    RUNNING              3   rmsprop         relu                                   32                 16                  5                 1          1.11714e-05         18                                              │
│ trial_3d9be    RUNNING              3   adam            relu                                   16                 16                  5                 1          2.04657e-05         20                                              │
│ trial_3d9be    RUNNING              3   adam            relu                                   32                 64                  3                 1          4.31547e-05         19                                              │
│ trial_3d9be    RUNNING              3   rmsprop         tanh                                   32                 16                  5                 0          5.99499e-06         22                                              │
│ trial_3d9be    TERMINATED           2   adam            tanh                                   32                 16                  5                 0          9.96166e-05         17        1            210.178         0.378778 │
│ trial_3d9be    TERMINATED           2   rmsprop         tanh                                   32                 32                  5                 1          0.000132925         20        1            184.162         0.479304 │
│ trial_3d9be    TERMINATED           2   adam            tanh                                   32                 16                  3                 0          4.35707e-05         17        1            202.869         0.413929 │
╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
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[36m(train_cnn_ray_tune pid=1858839)[0m 
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[36m(train_cnn_ray_tune pid=1858844)[0m 
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[36m(train_cnn_ray_tune pid=1858839)[0m Epoch 13/15[32m [repeated 6x across cluster][0m
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[36m(train_cnn_ray_tune pid=1858844)[0m 
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[36m(train_cnn_ray_tune pid=1858844)[0m 
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[36m(train_cnn_ray_tune pid=1858839)[0m 
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[36m(train_cnn_ray_tune pid=1858844)[0m 
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[36m(train_cnn_ray_tune pid=1858839)[0m 
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[36m(train_cnn_ray_tune pid=1858844)[0m 
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[36m(train_cnn_ray_tune pid=1858844)[0m 
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[36m(train_cnn_ray_tune pid=1858844)[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=1858844)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=1858844)[0m 
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[36m(train_cnn_ray_tune pid=1858820)[0m 
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[36m(train_cnn_ray_tune pid=1858844)[0m 
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[36m(train_cnn_ray_tune pid=1858844)[0m 
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[36m(train_cnn_ray_tune pid=1858844)[0m 
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Trial trial_3d9be finished iteration 1 at 2025-11-04 15:02:19. Total running time: 4min 33s
╭──────────────────────────────────────╮
│ Trial trial_3d9be result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             270.815 │
│ time_total_s                 270.815 │
│ training_iteration                 1 │
│ val_accuracy                 0.47602 │
╰──────────────────────────────────────╯

Trial trial_3d9be completed after 1 iterations at 2025-11-04 15:02:19. Total running time: 4min 33s
[36m(train_cnn_ray_tune pid=1858818)[0m 
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[36m(train_cnn_ray_tune pid=1858792)[0m 
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[36m(train_cnn_ray_tune pid=1858816)[0m 
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[36m(train_cnn_ray_tune pid=1858809)[0m 
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[36m(train_cnn_ray_tune pid=1858818)[0m 
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[36m(train_cnn_ray_tune pid=1858803)[0m 
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[36m(train_cnn_ray_tune pid=1858803)[0m Epoch 25/27[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=1858803)[0m 
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[36m(train_cnn_ray_tune pid=1858809)[0m 
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[36m(train_cnn_ray_tune pid=1858816)[0m 
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[36m(train_cnn_ray_tune pid=1858850)[0m 
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[36m(train_cnn_ray_tune pid=1858839)[0m 
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[36m(train_cnn_ray_tune pid=1858818)[0m 
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[36m(train_cnn_ray_tune pid=1858842)[0m 
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[36m(train_cnn_ray_tune pid=1858809)[0m 
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[36m(train_cnn_ray_tune pid=1858818)[0m 
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[36m(train_cnn_ray_tune pid=1858818)[0m Epoch 12/24[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=1858837)[0m 
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[36m(train_cnn_ray_tune pid=1858843)[0m 
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[36m(train_cnn_ray_tune pid=1858843)[0m 
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[36m(train_cnn_ray_tune pid=1858815)[0m 
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[36m(train_cnn_ray_tune pid=1858816)[0m 
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[36m(train_cnn_ray_tune pid=1858816)[0m 
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[36m(train_cnn_ray_tune pid=1858817)[0m 
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[36m(train_cnn_ray_tune pid=1858829)[0m 
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[36m(train_cnn_ray_tune pid=1858803)[0m 
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[36m(train_cnn_ray_tune pid=1858803)[0m 
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[36m(train_cnn_ray_tune pid=1858803)[0m Epoch 26/27
[36m(train_cnn_ray_tune pid=1858818)[0m 
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[36m(train_cnn_ray_tune pid=1858840)[0m 
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[36m(train_cnn_ray_tune pid=1858840)[0m Epoch 13/15
[36m(train_cnn_ray_tune pid=1858842)[0m 
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[36m(train_cnn_ray_tune pid=1858816)[0m 
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[36m(train_cnn_ray_tune pid=1858840)[0m 
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[36m(train_cnn_ray_tune pid=1858792)[0m 
[1m  1/665[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m53s[0m 80ms/step - accuracy: 0.5000 - loss: 0.8628
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[36m(train_cnn_ray_tune pid=1858820)[0m 
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[36m(train_cnn_ray_tune pid=1858816)[0m 
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[36m(train_cnn_ray_tune pid=1858838)[0m 
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[36m(train_cnn_ray_tune pid=1858817)[0m 
[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m22s[0m 473ms/step
[1m 6/49[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 11ms/step  
[36m(train_cnn_ray_tune pid=1858817)[0m 
[1m11/49[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 12ms/step
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[36m(train_cnn_ray_tune pid=1858817)[0m 
[1m22/49[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 11ms/step
[1m27/49[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 11ms/step
[36m(train_cnn_ray_tune pid=1858817)[0m 
[1m32/49[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 11ms/step
[1m37/49[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 12ms/step
[36m(train_cnn_ray_tune pid=1858842)[0m 
[1m333/665[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m9s[0m 30ms/step - accuracy: 0.4469 - loss: 1.2917 
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[36m(train_cnn_ray_tune pid=1858817)[0m 
[1m42/49[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 11ms/step
[1m46/49[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 12ms/step
[36m(train_cnn_ray_tune pid=1858809)[0m 
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[36m(train_cnn_ray_tune pid=1858817)[0m 
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 19ms/step
[36m(train_cnn_ray_tune pid=1858817)[0m 
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 19ms/step
[36m(train_cnn_ray_tune pid=1858817)[0m 
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[36m(train_cnn_ray_tune pid=1858817)[0m 
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[36m(train_cnn_ray_tune pid=1858817)[0m 
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[36m(train_cnn_ray_tune pid=1858817)[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=1858817)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=1858817)[0m 
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[36m(train_cnn_ray_tune pid=1858817)[0m 
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[36m(train_cnn_ray_tune pid=1858817)[0m 
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[36m(train_cnn_ray_tune pid=1858817)[0m 
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[36m(train_cnn_ray_tune pid=1858817)[0m 
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[36m(train_cnn_ray_tune pid=1858829)[0m 
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[36m(train_cnn_ray_tune pid=1858829)[0m Epoch 21/22[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=1858817)[0m 
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[36m(train_cnn_ray_tune pid=1858817)[0m 
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[36m(train_cnn_ray_tune pid=1858839)[0m 
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[36m(train_cnn_ray_tune pid=1858817)[0m 
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[36m(train_cnn_ray_tune pid=1858792)[0m 
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[36m(train_cnn_ray_tune pid=1858817)[0m 
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[36m(train_cnn_ray_tune pid=1858817)[0m 
[1m95/96[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 13ms/step
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Trial trial_3d9be finished iteration 1 at 2025-11-04 15:02:39. Total running time: 4min 53s
╭──────────────────────────────────────╮
│ Trial trial_3d9be result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             290.327 │
│ time_total_s                 290.327 │
│ training_iteration                 1 │
│ val_accuracy                 0.42806 │
╰──────────────────────────────────────╯

Trial trial_3d9be completed after 1 iterations at 2025-11-04 15:02:39. Total running time: 4min 53s
[36m(train_cnn_ray_tune pid=1858850)[0m 
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[36m(train_cnn_ray_tune pid=1858820)[0m 
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[36m(train_cnn_ray_tune pid=1858850)[0m 
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[36m(train_cnn_ray_tune pid=1858803)[0m 
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[36m(train_cnn_ray_tune pid=1858820)[0m 
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[36m(train_cnn_ray_tune pid=1858816)[0m 
[1m  1/665[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m53s[0m 81ms/step - accuracy: 0.5625 - loss: 1.2050[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=1858829)[0m 
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[1m190/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m3s[0m 28ms/step - accuracy: 0.4434 - loss: 1.3341
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[36m(train_cnn_ray_tune pid=1858843)[0m 
[1m665/665[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m21s[0m 32ms/step - accuracy: 0.6800 - loss: 0.7722 - val_accuracy: 0.5798 - val_loss: 1.0208[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=1858843)[0m Epoch 12/26[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=1858815)[0m 
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[1m 75/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m10s[0m 40ms/step - accuracy: 0.6344 - loss: 0.8783[32m [repeated 113x across cluster][0m
[36m(train_cnn_ray_tune pid=1858816)[0m 
[1m 80/665[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m15s[0m 26ms/step - accuracy: 0.4926 - loss: 1.1873[32m [repeated 81x across cluster][0m
[36m(train_cnn_ray_tune pid=1858842)[0m 
[1m601/665[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m1s[0m 30ms/step - accuracy: 0.4474 - loss: 1.2879
[1m603/665[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m1s[0m 30ms/step - accuracy: 0.4474 - loss: 1.2878[32m [repeated 278x across cluster][0m
[36m(train_cnn_ray_tune pid=1858815)[0m 
[1m102/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m9s[0m 40ms/step - accuracy: 0.6351 - loss: 0.8784[32m [repeated 205x across cluster][0m
[36m(train_cnn_ray_tune pid=1858820)[0m 
[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m24s[0m 74ms/step - accuracy: 0.2500 - loss: 1.6276
[1m  3/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 32ms/step - accuracy: 0.2708 - loss: 1.6305
[36m(train_cnn_ray_tune pid=1858829)[0m 
[1m297/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m1s[0m 28ms/step - accuracy: 0.4396 - loss: 1.3364
[1m299/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 28ms/step - accuracy: 0.4396 - loss: 1.3364
[1m301/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 28ms/step - accuracy: 0.4395 - loss: 1.3364

Trial status: 15 RUNNING | 5 TERMINATED
Current time: 2025-11-04 15:02:46. Total running time: 5min 1s
Logical resource usage: 15.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     num_resblocks     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_3d9be    RUNNING              3   rmsprop         tanh                                   16                 16                  3                 0          9.30537e-05         19                                              │
│ trial_3d9be    RUNNING              2   adam            tanh                                   16                 32                  5                 0          8.54171e-06         25                                              │
│ trial_3d9be    RUNNING              2   rmsprop         relu                                   32                 16                  5                 1          1.02234e-05         27                                              │
│ trial_3d9be    RUNNING              3   adam            relu                                   32                 64                  3                 0          4.14473e-05         29                                              │
│ trial_3d9be    RUNNING              2   rmsprop         tanh                                   32                 64                  5                 0          9.17858e-06         22                                              │
│ trial_3d9be    RUNNING              2   adam            relu                                   16                 64                  3                 0          3.03615e-05         15                                              │
│ trial_3d9be    RUNNING              3   adam            tanh                                   16                 16                  3                 0          5.25384e-06         15                                              │
│ trial_3d9be    RUNNING              2   rmsprop         tanh                                   16                 32                  5                 1          9.49597e-06         15                                              │
│ trial_3d9be    RUNNING              3   adam            tanh                                   16                 16                  3                 0          9.81418e-05         21                                              │
│ trial_3d9be    RUNNING              2   adam            relu                                   16                 64                  5                 1          0.000124913         26                                              │
│ trial_3d9be    RUNNING              2   adam            relu                                   16                 32                  3                 1          6.24019e-06         15                                              │
│ trial_3d9be    RUNNING              2   adam            tanh                                   16                 64                  3                 1          8.38585e-05         24                                              │
│ trial_3d9be    RUNNING              3   adam            relu                                   16                 16                  5                 1          2.04657e-05         20                                              │
│ trial_3d9be    RUNNING              3   adam            relu                                   32                 64                  3                 1          4.31547e-05         19                                              │
│ trial_3d9be    RUNNING              3   rmsprop         tanh                                   32                 16                  5                 0          5.99499e-06         22                                              │
│ trial_3d9be    TERMINATED           3   adam            tanh                                   32                 16                  3                 0          1.1283e-05          20        1            290.327         0.428055 │
│ trial_3d9be    TERMINATED           2   adam            tanh                                   32                 16                  5                 0          9.96166e-05         17        1            210.178         0.378778 │
│ trial_3d9be    TERMINATED           2   rmsprop         tanh                                   32                 32                  5                 1          0.000132925         20        1            184.162         0.479304 │
│ trial_3d9be    TERMINATED           3   rmsprop         relu                                   32                 16                  5                 1          1.11714e-05         18        1            270.815         0.476018 │
│ trial_3d9be    TERMINATED           2   adam            tanh                                   32                 16                  3                 0          4.35707e-05         17        1            202.869         0.413929 │
╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
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[36m(train_cnn_ray_tune pid=1858838)[0m 
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[36m(train_cnn_ray_tune pid=1858837)[0m 
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[36m(train_cnn_ray_tune pid=1858837)[0m Epoch 12/15[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=1858816)[0m 
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[36m(train_cnn_ray_tune pid=1858843)[0m 
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[36m(train_cnn_ray_tune pid=1858803)[0m 
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Trial trial_3d9be finished iteration 1 at 2025-11-04 15:02:51. Total running time: 5min 5s
[36m(train_cnn_ray_tune pid=1858803)[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=1858803)[0m   _log_deprecation_warning(
╭──────────────────────────────────────╮
│ Trial trial_3d9be result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s              302.91 │
│ time_total_s                  302.91 │
│ training_iteration                 1 │
│ val_accuracy                 0.49277 │
╰──────────────────────────────────────╯

Trial trial_3d9be completed after 1 iterations at 2025-11-04 15:02:51. Total running time: 5min 5s
[36m(train_cnn_ray_tune pid=1858837)[0m 
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[36m(train_cnn_ray_tune pid=1858843)[0m 
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[36m(train_cnn_ray_tune pid=1858850)[0m 
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[36m(train_cnn_ray_tune pid=1858839)[0m 
[1m665/665[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m18s[0m 27ms/step - accuracy: 0.3960 - loss: 1.4959 - val_accuracy: 0.4639 - val_loss: 1.2302[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=1858839)[0m Epoch 15/15[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=1858842)[0m 
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[36m(train_cnn_ray_tune pid=1858839)[0m 
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[36m(train_cnn_ray_tune pid=1858816)[0m 
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[36m(train_cnn_ray_tune pid=1858838)[0m 
[1m599/665[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m1s[0m 26ms/step - accuracy: 0.4494 - loss: 1.1630[32m [repeated 133x across cluster][0m
[36m(train_cnn_ray_tune pid=1858820)[0m 
[1m  5/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 29ms/step - accuracy: 0.3271 - loss: 1.5478 
[1m  7/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 31ms/step - accuracy: 0.3259 - loss: 1.5380[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=1858842)[0m 
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[36m(train_cnn_ray_tune pid=1858815)[0m 
[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m28s[0m 87ms/step - accuracy: 0.6875 - loss: 0.6933
[36m(train_cnn_ray_tune pid=1858820)[0m 
[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m27s[0m 83ms/step - accuracy: 0.3438 - loss: 1.5564
[1m  3/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 30ms/step - accuracy: 0.3368 - loss: 1.5541[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=1858829)[0m 
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m10s[0m 31ms/step - accuracy: 0.4361 - loss: 1.3076 - val_accuracy: 0.4681 - val_loss: 1.2531[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=1858820)[0m Epoch 21/22[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=1858829)[0m 
[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m14s[0m 301ms/step
[1m 7/49[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 11ms/step  
[36m(train_cnn_ray_tune pid=1858792)[0m 
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[36m(train_cnn_ray_tune pid=1858829)[0m 
[1m11/49[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 12ms/step
[1m15/49[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 12ms/step
[36m(train_cnn_ray_tune pid=1858792)[0m 
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[36m(train_cnn_ray_tune pid=1858829)[0m 
[1m20/49[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 12ms/step
[1m24/49[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 12ms/step
[36m(train_cnn_ray_tune pid=1858842)[0m 
[1m351/665[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m8s[0m 28ms/step - accuracy: 0.4463 - loss: 1.2639
[1m353/665[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m8s[0m 28ms/step - accuracy: 0.4464 - loss: 1.2639
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[36m(train_cnn_ray_tune pid=1858829)[0m 
[1m28/49[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 13ms/step
[1m34/49[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 12ms/step
[36m(train_cnn_ray_tune pid=1858829)[0m 
[1m38/49[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 13ms/step
[1m43/49[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 12ms/step
[36m(train_cnn_ray_tune pid=1858829)[0m 
[1m48/49[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 12ms/step
[36m(train_cnn_ray_tune pid=1858829)[0m 
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 18ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 19ms/step
[36m(train_cnn_ray_tune pid=1858829)[0m 
[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 46ms/step
[1m 5/96[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 14ms/step
[36m(train_cnn_ray_tune pid=1858842)[0m 
[1m383/665[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m7s[0m 28ms/step - accuracy: 0.4466 - loss: 1.2635
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[36m(train_cnn_ray_tune pid=1858809)[0m 
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[36m(train_cnn_ray_tune pid=1858829)[0m 
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[36m(train_cnn_ray_tune pid=1858829)[0m 
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[36m(train_cnn_ray_tune pid=1858829)[0m 
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[36m(train_cnn_ray_tune pid=1858829)[0m 
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[36m(train_cnn_ray_tune pid=1858829)[0m 
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[36m(train_cnn_ray_tune pid=1858829)[0m 
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Trial trial_3d9be finished iteration 1 at 2025-11-04 15:03:01. Total running time: 5min 15s
[36m(train_cnn_ray_tune pid=1858829)[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=1858829)[0m   _log_deprecation_warning(
╭──────────────────────────────────────╮
│ Trial trial_3d9be result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             312.318 │
│ time_total_s                 312.318 │
│ training_iteration                 1 │
│ val_accuracy                 0.46813 │
╰──────────────────────────────────────╯

Trial trial_3d9be completed after 1 iterations at 2025-11-04 15:03:01. Total running time: 5min 15s
[36m(train_cnn_ray_tune pid=1858829)[0m 
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[36m(train_cnn_ray_tune pid=1858850)[0m 
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[36m(train_cnn_ray_tune pid=1858850)[0m Epoch 18/19[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=1858816)[0m 
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[36m(train_cnn_ray_tune pid=1858850)[0m 
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[36m(train_cnn_ray_tune pid=1858843)[0m 
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[36m(train_cnn_ray_tune pid=1858820)[0m 
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[36m(train_cnn_ray_tune pid=1858842)[0m 
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[36m(train_cnn_ray_tune pid=1858838)[0m 
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[36m(train_cnn_ray_tune pid=1858818)[0m 
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[36m(train_cnn_ray_tune pid=1858839)[0m 
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[36m(train_cnn_ray_tune pid=1858839)[0m 
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[36m(train_cnn_ray_tune pid=1858839)[0m 
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[36m(train_cnn_ray_tune pid=1858839)[0m 
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[36m(train_cnn_ray_tune pid=1858815)[0m 
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[36m(train_cnn_ray_tune pid=1858815)[0m Epoch 18/29[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=1858839)[0m 
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[36m(train_cnn_ray_tune pid=1858839)[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=1858839)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=1858818)[0m 
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[36m(train_cnn_ray_tune pid=1858840)[0m 
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[36m(train_cnn_ray_tune pid=1858839)[0m 
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[36m(train_cnn_ray_tune pid=1858839)[0m 
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[36m(train_cnn_ray_tune pid=1858837)[0m 
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[36m(train_cnn_ray_tune pid=1858839)[0m 
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[36m(train_cnn_ray_tune pid=1858842)[0m 
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[36m(train_cnn_ray_tune pid=1858839)[0m 
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[36m(train_cnn_ray_tune pid=1858839)[0m 
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[36m(train_cnn_ray_tune pid=1858839)[0m 
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[36m(train_cnn_ray_tune pid=1858839)[0m 
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Trial trial_3d9be finished iteration 1 at 2025-11-04 15:03:10. Total running time: 5min 24s
╭──────────────────────────────────────╮
│ Trial trial_3d9be result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             320.997 │
│ time_total_s                 320.997 │
│ training_iteration                 1 │
│ val_accuracy                 0.46912 │
╰──────────────────────────────────────╯

Trial trial_3d9be completed after 1 iterations at 2025-11-04 15:03:10. Total running time: 5min 24s
[36m(train_cnn_ray_tune pid=1858839)[0m 
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[36m(train_cnn_ray_tune pid=1858820)[0m 
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[36m(train_cnn_ray_tune pid=1858816)[0m 
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[36m(train_cnn_ray_tune pid=1858842)[0m 
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[36m(train_cnn_ray_tune pid=1858809)[0m 
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[36m(train_cnn_ray_tune pid=1858792)[0m 
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[36m(train_cnn_ray_tune pid=1858792)[0m Epoch 14/19[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=1858842)[0m 
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[36m(train_cnn_ray_tune pid=1858818)[0m 
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[36m(train_cnn_ray_tune pid=1858842)[0m 
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[36m(train_cnn_ray_tune pid=1858842)[0m 
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[36m(train_cnn_ray_tune pid=1858850)[0m 
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[36m(train_cnn_ray_tune pid=1858842)[0m 
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[36m(train_cnn_ray_tune pid=1858815)[0m 
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[36m(train_cnn_ray_tune pid=1858792)[0m 
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[36m(train_cnn_ray_tune pid=1858850)[0m 
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[36m(train_cnn_ray_tune pid=1858820)[0m 
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[36m(train_cnn_ray_tune pid=1858820)[0m 
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[36m(train_cnn_ray_tune pid=1858820)[0m 
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[36m(train_cnn_ray_tune pid=1858820)[0m 
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[36m(train_cnn_ray_tune pid=1858820)[0m 
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[36m(train_cnn_ray_tune pid=1858820)[0m 
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Trial status: 12 RUNNING | 8 TERMINATED
Current time: 2025-11-04 15:03:16. Total running time: 5min 31s
Logical resource usage: 12.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     num_resblocks     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_3d9be    RUNNING              3   rmsprop         tanh                                   16                 16                  3                 0          9.30537e-05         19                                              │
│ trial_3d9be    RUNNING              2   adam            tanh                                   16                 32                  5                 0          8.54171e-06         25                                              │
│ trial_3d9be    RUNNING              3   adam            relu                                   32                 64                  3                 0          4.14473e-05         29                                              │
│ trial_3d9be    RUNNING              2   adam            relu                                   16                 64                  3                 0          3.03615e-05         15                                              │
│ trial_3d9be    RUNNING              3   adam            tanh                                   16                 16                  3                 0          5.25384e-06         15                                              │
│ trial_3d9be    RUNNING              2   rmsprop         tanh                                   16                 32                  5                 1          9.49597e-06         15                                              │
│ trial_3d9be    RUNNING              3   adam            tanh                                   16                 16                  3                 0          9.81418e-05         21                                              │
│ trial_3d9be    RUNNING              2   adam            relu                                   16                 64                  5                 1          0.000124913         26                                              │
│ trial_3d9be    RUNNING              2   adam            tanh                                   16                 64                  3                 1          8.38585e-05         24                                              │
│ trial_3d9be    RUNNING              3   adam            relu                                   16                 16                  5                 1          2.04657e-05         20                                              │
│ trial_3d9be    RUNNING              3   adam            relu                                   32                 64                  3                 1          4.31547e-05         19                                              │
│ trial_3d9be    RUNNING              3   rmsprop         tanh                                   32                 16                  5                 0          5.99499e-06         22                                              │
│ trial_3d9be    TERMINATED           2   rmsprop         relu                                   32                 16                  5                 1          1.02234e-05         27        1            302.91          0.492773 │
│ trial_3d9be    TERMINATED           2   rmsprop         tanh                                   32                 64                  5                 0          9.17858e-06         22        1            312.318         0.468134 │
│ trial_3d9be    TERMINATED           3   adam            tanh                                   32                 16                  3                 0          1.1283e-05          20        1            290.327         0.428055 │
│ trial_3d9be    TERMINATED           2   adam            tanh                                   32                 16                  5                 0          9.96166e-05         17        1            210.178         0.378778 │
│ trial_3d9be    TERMINATED           2   adam            relu                                   16                 32                  3                 1          6.24019e-06         15        1            320.997         0.46912  │
│ trial_3d9be    TERMINATED           2   rmsprop         tanh                                   32                 32                  5                 1          0.000132925         20        1            184.162         0.479304 │
│ trial_3d9be    TERMINATED           3   rmsprop         relu                                   32                 16                  5                 1          1.11714e-05         18        1            270.815         0.476018 │
│ trial_3d9be    TERMINATED           2   adam            tanh                                   32                 16                  3                 0          4.35707e-05         17        1            202.869         0.413929 │
╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=1858820)[0m 
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[36m(train_cnn_ray_tune pid=1858820)[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=1858820)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=1858820)[0m 
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[36m(train_cnn_ray_tune pid=1858820)[0m 
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[36m(train_cnn_ray_tune pid=1858820)[0m 
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Trial trial_3d9be finished iteration 1 at 2025-11-04 15:03:18. Total running time: 5min 32s
╭──────────────────────────────────────╮
│ Trial trial_3d9be result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             329.552 │
│ time_total_s                 329.552 │
│ training_iteration                 1 │
│ val_accuracy                 0.41623 │
╰──────────────────────────────────────╯

Trial trial_3d9be completed after 1 iterations at 2025-11-04 15:03:18. Total running time: 5min 32s
[36m(train_cnn_ray_tune pid=1858820)[0m 
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[36m(train_cnn_ray_tune pid=1858816)[0m 
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[36m(train_cnn_ray_tune pid=1858816)[0m Epoch 14/15[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=1858838)[0m 
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[36m(train_cnn_ray_tune pid=1858816)[0m 
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[36m(train_cnn_ray_tune pid=1858838)[0m 
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[36m(train_cnn_ray_tune pid=1858843)[0m 
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[36m(train_cnn_ray_tune pid=1858816)[0m 
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[36m(train_cnn_ray_tune pid=1858792)[0m 
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[36m(train_cnn_ray_tune pid=1858840)[0m 
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[36m(train_cnn_ray_tune pid=1858840)[0m 
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[36m(train_cnn_ray_tune pid=1858840)[0m 
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[36m(train_cnn_ray_tune pid=1858840)[0m 
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[36m(train_cnn_ray_tune pid=1858840)[0m 
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[36m(train_cnn_ray_tune pid=1858840)[0m 
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Trial trial_3d9be finished iteration 1 at 2025-11-04 15:03:22. Total running time: 5min 36s
╭──────────────────────────────────────╮
│ Trial trial_3d9be result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s              333.35 │
│ time_total_s                  333.35 │
│ training_iteration                 1 │
│ val_accuracy                 0.43003 │
╰──────────────────────────────────────╯

Trial trial_3d9be completed after 1 iterations at 2025-11-04 15:03:22. Total running time: 5min 36s
[36m(train_cnn_ray_tune pid=1858840)[0m 
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 7ms/step
[36m(train_cnn_ray_tune pid=1858809)[0m 
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[36m(train_cnn_ray_tune pid=1858809)[0m Epoch 17/25[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=1858818)[0m 
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[36m(train_cnn_ray_tune pid=1858843)[0m 
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[36m(train_cnn_ray_tune pid=1858850)[0m 
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[36m(train_cnn_ray_tune pid=1858809)[0m 
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[36m(train_cnn_ray_tune pid=1858837)[0m 
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[36m(train_cnn_ray_tune pid=1858816)[0m 
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[36m(train_cnn_ray_tune pid=1858792)[0m 
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[36m(train_cnn_ray_tune pid=1858850)[0m 
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[36m(train_cnn_ray_tune pid=1858850)[0m 
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[36m(train_cnn_ray_tune pid=1858850)[0m 
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[36m(train_cnn_ray_tune pid=1858850)[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=1858850)[0m   _log_deprecation_warning([32m [repeated 2x across cluster][0m
Trial trial_3d9be finished iteration 1 at 2025-11-04 15:03:26. Total running time: 5min 40s
╭──────────────────────────────────────╮
│ Trial trial_3d9be result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             337.404 │
│ time_total_s                 337.404 │
│ training_iteration                 1 │
│ val_accuracy                 0.56045 │
╰──────────────────────────────────────╯

Trial trial_3d9be completed after 1 iterations at 2025-11-04 15:03:26. Total running time: 5min 40s
[36m(train_cnn_ray_tune pid=1858842)[0m 
[1m  1/665[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m33s[0m 50ms/step - accuracy: 0.5625 - loss: 0.8545[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=1858850)[0m 
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 13ms/step
[36m(train_cnn_ray_tune pid=1858816)[0m 
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[36m(train_cnn_ray_tune pid=1858816)[0m 
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[36m(train_cnn_ray_tune pid=1858843)[0m 
[1m665/665[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m10s[0m 14ms/step - accuracy: 0.7286 - loss: 0.6732 - val_accuracy: 0.5769 - val_loss: 1.0490[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=1858838)[0m Epoch 15/21[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=1858815)[0m 
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[36m(train_cnn_ray_tune pid=1858843)[0m 
[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 264ms/step
[1m10/49[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 6ms/step   
[36m(train_cnn_ray_tune pid=1858842)[0m 
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[36m(train_cnn_ray_tune pid=1858843)[0m 
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[36m(train_cnn_ray_tune pid=1858816)[0m 
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[36m(train_cnn_ray_tune pid=1858816)[0m 
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[36m(train_cnn_ray_tune pid=1858843)[0m 
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Trial trial_3d9be finished iteration 1 at 2025-11-04 15:03:30. Total running time: 5min 44s
╭──────────────────────────────────────╮
│ Trial trial_3d9be result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             341.576 │
│ time_total_s                 341.576 │
│ training_iteration                 1 │
│ val_accuracy                 0.57687 │
╰──────────────────────────────────────╯

Trial trial_3d9be completed after 1 iterations at 2025-11-04 15:03:30. Total running time: 5min 44s
[36m(train_cnn_ray_tune pid=1858843)[0m 
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[36m(train_cnn_ray_tune pid=1858792)[0m   _log_deprecation_warning([32m [repeated 2x across cluster][0m
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[36m(train_cnn_ray_tune pid=1858842)[0m 
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Trial trial_3d9be finished iteration 1 at 2025-11-04 15:03:35. Total running time: 5min 49s
╭──────────────────────────────────────╮
│ Trial trial_3d9be result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             346.657 │
│ time_total_s                 346.657 │
│ training_iteration                 1 │
│ val_accuracy                 0.47898 │
╰──────────────────────────────────────╯

Trial trial_3d9be completed after 1 iterations at 2025-11-04 15:03:35. Total running time: 5min 49s
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[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 33ms/step
[1m12/96[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 5ms/step 

Trial trial_3d9be finished iteration 1 at 2025-11-04 15:03:39. Total running time: 5min 53s
╭──────────────────────────────────────╮
│ Trial trial_3d9be result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             350.071 │
│ time_total_s                 350.071 │
│ training_iteration                 1 │
│ val_accuracy                  0.5184 │
╰──────────────────────────────────────╯

Trial trial_3d9be completed after 1 iterations at 2025-11-04 15:03:39. Total running time: 5min 53s
[36m(train_cnn_ray_tune pid=1858818)[0m 
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[36m(train_cnn_ray_tune pid=1858818)[0m Epoch 17/24[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=1858838)[0m 
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[36m(train_cnn_ray_tune pid=1858837)[0m 
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[36m(train_cnn_ray_tune pid=1858809)[0m 
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[36m(train_cnn_ray_tune pid=1858837)[0m 
[1m14/49[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 4ms/step   
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[36m(train_cnn_ray_tune pid=1858809)[0m 
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[36m(train_cnn_ray_tune pid=1858809)[0m 
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[36m(train_cnn_ray_tune pid=1858818)[0m 
[1m  1/665[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m31s[0m 47ms/step - accuracy: 0.6875 - loss: 1.1372[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=1858809)[0m 
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[36m(train_cnn_ray_tune pid=1858809)[0m 
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[36m(train_cnn_ray_tune pid=1858815)[0m 
[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m15s[0m 47ms/step - accuracy: 0.4062 - loss: 1.0878
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[36m(train_cnn_ray_tune pid=1858837)[0m 
[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 35ms/step
[36m(train_cnn_ray_tune pid=1858809)[0m 
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[36m(train_cnn_ray_tune pid=1858809)[0m 
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[36m(train_cnn_ray_tune pid=1858837)[0m 
[1m11/96[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 5ms/step 
[1m23/96[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 5ms/step
[36m(train_cnn_ray_tune pid=1858842)[0m 
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[36m(train_cnn_ray_tune pid=1858815)[0m 
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Trial trial_3d9be finished iteration 1 at 2025-11-04 15:03:40. Total running time: 5min 55s
[36m(train_cnn_ray_tune pid=1858837)[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=1858837)[0m   _log_deprecation_warning([32m [repeated 2x across cluster][0m
╭──────────────────────────────────────╮
│ Trial trial_3d9be result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             352.268 │
│ time_total_s                 352.268 │
│ training_iteration                 1 │
│ val_accuracy                 0.37451 │
╰──────────────────────────────────────╯

Trial trial_3d9be completed after 1 iterations at 2025-11-04 15:03:40. Total running time: 5min 55s
[36m(train_cnn_ray_tune pid=1858837)[0m 
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step[32m [repeated 14x across cluster][0m
[36m(train_cnn_ray_tune pid=1858809)[0m 
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[36m(train_cnn_ray_tune pid=1858818)[0m 
[1m492/665[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m1s[0m 8ms/step - accuracy: 0.6331 - loss: 0.8772
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[36m(train_cnn_ray_tune pid=1858838)[0m 
[1m665/665[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 10ms/step - accuracy: 0.4740 - loss: 1.1018 - val_accuracy: 0.4800 - val_loss: 1.0954
[36m(train_cnn_ray_tune pid=1858842)[0m 
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[36m(train_cnn_ray_tune pid=1858809)[0m 
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[36m(train_cnn_ray_tune pid=1858842)[0m 
[1m665/665[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m8s[0m 11ms/step - accuracy: 0.4515 - loss: 1.2306 - val_accuracy: 0.5049 - val_loss: 1.1148[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=1858809)[0m Epoch 20/25[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=1858818)[0m 
[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 240ms/step
[36m(train_cnn_ray_tune pid=1858818)[0m 
[1m659/665[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 8ms/step - accuracy: 0.6336 - loss: 0.8743[32m [repeated 18x across cluster][0m
[36m(train_cnn_ray_tune pid=1858818)[0m 
[1m14/49[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 4ms/step   
[1m29/49[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 4ms/step
[36m(train_cnn_ray_tune pid=1858809)[0m 
[1m  1/665[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m28s[0m 43ms/step - accuracy: 0.2500 - loss: 2.1311[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1858815)[0m 
[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 38ms/step - accuracy: 0.5312 - loss: 1.1437
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[36m(train_cnn_ray_tune pid=1858818)[0m 
[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 32ms/step

Trial trial_3d9be finished iteration 1 at 2025-11-04 15:03:45. Total running time: 5min 59s
╭──────────────────────────────────────╮
│ Trial trial_3d9be result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             356.453 │
│ time_total_s                 356.453 │
│ training_iteration                 1 │
│ val_accuracy                 0.50558 │
╰──────────────────────────────────────╯

Trial trial_3d9be completed after 1 iterations at 2025-11-04 15:03:45. Total running time: 5min 59s
[36m(train_cnn_ray_tune pid=1858818)[0m 
[1m15/96[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 4ms/step 
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[36m(train_cnn_ray_tune pid=1858815)[0m 
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[36m(train_cnn_ray_tune pid=1858815)[0m 
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[36m(train_cnn_ray_tune pid=1858809)[0m 
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[36m(train_cnn_ray_tune pid=1858818)[0m 
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Trial status: 16 TERMINATED | 4 RUNNING
Current time: 2025-11-04 15:03:47. Total running time: 6min 1s
Logical resource usage: 4.0/20 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_3d9be    RUNNING              2   adam            tanh                                   16                 32                  5                 0          8.54171e-06         25                                              │
│ trial_3d9be    RUNNING              3   adam            relu                                   32                 64                  3                 0          4.14473e-05         29                                              │
│ trial_3d9be    RUNNING              3   adam            tanh                                   16                 16                  3                 0          9.81418e-05         21                                              │
│ trial_3d9be    RUNNING              3   adam            relu                                   16                 16                  5                 1          2.04657e-05         20                                              │
│ trial_3d9be    TERMINATED           3   rmsprop         tanh                                   16                 16                  3                 0          9.30537e-05         19        1            346.657         0.478975 │
│ trial_3d9be    TERMINATED           2   rmsprop         relu                                   32                 16                  5                 1          1.02234e-05         27        1            302.91          0.492773 │
│ trial_3d9be    TERMINATED           2   rmsprop         tanh                                   32                 64                  5                 0          9.17858e-06         22        1            312.318         0.468134 │
│ trial_3d9be    TERMINATED           3   adam            tanh                                   32                 16                  3                 0          1.1283e-05          20        1            290.327         0.428055 │
│ trial_3d9be    TERMINATED           2   adam            relu                                   16                 64                  3                 0          3.03615e-05         15        1            350.071         0.518397 │
│ trial_3d9be    TERMINATED           3   adam            tanh                                   16                 16                  3                 0          5.25384e-06         15        1            352.268         0.374507 │
│ trial_3d9be    TERMINATED           2   adam            tanh                                   32                 16                  5                 0          9.96166e-05         17        1            210.178         0.378778 │
│ trial_3d9be    TERMINATED           2   rmsprop         tanh                                   16                 32                  5                 1          9.49597e-06         15        1            333.35          0.430026 │
│ trial_3d9be    TERMINATED           2   adam            relu                                   16                 64                  5                 1          0.000124913         26        1            341.576         0.576873 │
│ trial_3d9be    TERMINATED           2   adam            relu                                   16                 32                  3                 1          6.24019e-06         15        1            320.997         0.46912  │
│ trial_3d9be    TERMINATED           2   adam            tanh                                   16                 64                  3                 1          8.38585e-05         24        1            356.453         0.505585 │
│ trial_3d9be    TERMINATED           2   rmsprop         tanh                                   32                 32                  5                 1          0.000132925         20        1            184.162         0.479304 │
│ trial_3d9be    TERMINATED           3   rmsprop         relu                                   32                 16                  5                 1          1.11714e-05         18        1            270.815         0.476018 │
│ trial_3d9be    TERMINATED           3   adam            relu                                   32                 64                  3                 1          4.31547e-05         19        1            337.404         0.560447 │
│ trial_3d9be    TERMINATED           3   rmsprop         tanh                                   32                 16                  5                 0          5.99499e-06         22        1            329.552         0.416229 │
│ trial_3d9be    TERMINATED           2   adam            tanh                                   32                 16                  3                 0          4.35707e-05         17        1            202.869         0.413929 │
╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=1858838)[0m 
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[36m(train_cnn_ray_tune pid=1858842)[0m 
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[36m(train_cnn_ray_tune pid=1858815)[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=1858815)[0m   _log_deprecation_warning([32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1858842)[0m 
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[36m(train_cnn_ray_tune pid=1858815)[0m 
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[36m(train_cnn_ray_tune pid=1858838)[0m Epoch 18/21[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=1858815)[0m 
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[36m(train_cnn_ray_tune pid=1858842)[0m 
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[36m(train_cnn_ray_tune pid=1858838)[0m 
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[36m(train_cnn_ray_tune pid=1858815)[0m 
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[36m(train_cnn_ray_tune pid=1858815)[0m 
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[36m(train_cnn_ray_tune pid=1858815)[0m 
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[36m(train_cnn_ray_tune pid=1858815)[0m 
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[36m(train_cnn_ray_tune pid=1858815)[0m 
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[36m(train_cnn_ray_tune pid=1858838)[0m 
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[36m(train_cnn_ray_tune pid=1858815)[0m 
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[36m(train_cnn_ray_tune pid=1858815)[0m 
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[36m(train_cnn_ray_tune pid=1858815)[0m 
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Trial trial_3d9be finished iteration 1 at 2025-11-04 15:03:52. Total running time: 6min 7s
╭──────────────────────────────────────╮
│ Trial trial_3d9be result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             363.988 │
│ time_total_s                 363.988 │
│ training_iteration                 1 │
│ val_accuracy                 0.56373 │
╰──────────────────────────────────────╯

Trial trial_3d9be completed after 1 iterations at 2025-11-04 15:03:52. Total running time: 6min 7s
[36m(train_cnn_ray_tune pid=1858815)[0m 
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[36m(train_cnn_ray_tune pid=1858842)[0m 
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[36m(train_cnn_ray_tune pid=1858842)[0m 
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[36m(train_cnn_ray_tune pid=1858815)[0m 
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[36m(train_cnn_ray_tune pid=1858838)[0m Epoch 19/21[32m [repeated 3x across cluster][0m
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[36m(train_cnn_ray_tune pid=1858815)[0m 
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[36m(train_cnn_ray_tune pid=1858838)[0m 
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[36m(train_cnn_ray_tune pid=1858842)[0m 
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[36m(train_cnn_ray_tune pid=1858809)[0m 
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[36m(train_cnn_ray_tune pid=1858809)[0m Epoch 24/25[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=1858809)[0m 
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[36m(train_cnn_ray_tune pid=1858809)[0m 
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[36m(train_cnn_ray_tune pid=1858809)[0m 
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[36m(train_cnn_ray_tune pid=1858838)[0m 
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[36m(train_cnn_ray_tune pid=1858809)[0m 
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[36m(train_cnn_ray_tune pid=1858809)[0m Epoch 25/25[32m [repeated 3x across cluster][0m
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[36m(train_cnn_ray_tune pid=1858838)[0m 
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[36m(train_cnn_ray_tune pid=1858838)[0m 
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[36m(train_cnn_ray_tune pid=1858838)[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=1858838)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=1858842)[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=1858842)[0m   _log_deprecation_warning(
2025-11-04 15:04:05,675	INFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/mnt/nvme1n2/git/uniovi-simur-wearablepermed-data/output/Paper_results/cases_dataset_C/case_C_CAPTURE24_acc_gyr_superclasses_CPA_METs/CAPTURE24_hyperparameters_tuning' in 0.0066s.
[36m(train_cnn_ray_tune pid=1858842)[0m 
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Trial trial_3d9be finished iteration 1 at 2025-11-04 15:04:05. Total running time: 6min 19s
╭──────────────────────────────────────╮
│ Trial trial_3d9be result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             376.346 │
│ time_total_s                 376.346 │
│ training_iteration                 1 │
│ val_accuracy                 0.46025 │
╰──────────────────────────────────────╯

Trial trial_3d9be completed after 1 iterations at 2025-11-04 15:04:05. Total running time: 6min 19s
[36m(train_cnn_ray_tune pid=1858842)[0m 
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[36m(train_cnn_ray_tune pid=1858809)[0m 
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Trial trial_3d9be finished iteration 1 at 2025-11-04 15:04:05. Total running time: 6min 19s
╭──────────────────────────────────────╮
│ Trial trial_3d9be result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             376.249 │
│ time_total_s                 376.249 │
│ training_iteration                 1 │
│ val_accuracy                 0.51018 │
╰──────────────────────────────────────╯

Trial trial_3d9be completed after 1 iterations at 2025-11-04 15:04:05. Total running time: 6min 19s
[36m(train_cnn_ray_tune pid=1858809)[0m 
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[36m(train_cnn_ray_tune pid=1858809)[0m 
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Trial trial_3d9be finished iteration 1 at 2025-11-04 15:04:05. Total running time: 6min 19s
╭──────────────────────────────────────╮
│ Trial trial_3d9be result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             376.799 │
│ time_total_s                 376.799 │
│ training_iteration                 1 │
│ val_accuracy                  0.4366 │
╰──────────────────────────────────────╯

Trial trial_3d9be completed after 1 iterations at 2025-11-04 15:04:05. Total running time: 6min 19s

Trial status: 20 TERMINATED
Current time: 2025-11-04 15:04:05. Total running time: 6min 19s
Logical resource usage: 1.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
I0000 00:00:1762265045.811340 1857190 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13766 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
╭────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ 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_3d9be    TERMINATED           3   rmsprop         tanh                                   16                 16                  3                 0          9.30537e-05         19        1            346.657         0.478975 │
│ trial_3d9be    TERMINATED           2   adam            tanh                                   16                 32                  5                 0          8.54171e-06         25        1            376.799         0.436597 │
│ trial_3d9be    TERMINATED           2   rmsprop         relu                                   32                 16                  5                 1          1.02234e-05         27        1            302.91          0.492773 │
│ trial_3d9be    TERMINATED           3   adam            relu                                   32                 64                  3                 0          4.14473e-05         29        1            363.988         0.563732 │
│ trial_3d9be    TERMINATED           2   rmsprop         tanh                                   32                 64                  5                 0          9.17858e-06         22        1            312.318         0.468134 │
│ trial_3d9be    TERMINATED           3   adam            tanh                                   32                 16                  3                 0          1.1283e-05          20        1            290.327         0.428055 │
│ trial_3d9be    TERMINATED           2   adam            relu                                   16                 64                  3                 0          3.03615e-05         15        1            350.071         0.518397 │
│ trial_3d9be    TERMINATED           3   adam            tanh                                   16                 16                  3                 0          5.25384e-06         15        1            352.268         0.374507 │
│ trial_3d9be    TERMINATED           2   adam            tanh                                   32                 16                  5                 0          9.96166e-05         17        1            210.178         0.378778 │
│ trial_3d9be    TERMINATED           2   rmsprop         tanh                                   16                 32                  5                 1          9.49597e-06         15        1            333.35          0.430026 │
│ trial_3d9be    TERMINATED           3   adam            tanh                                   16                 16                  3                 0          9.81418e-05         21        1            376.346         0.46025  │
│ trial_3d9be    TERMINATED           2   adam            relu                                   16                 64                  5                 1          0.000124913         26        1            341.576         0.576873 │
│ trial_3d9be    TERMINATED           2   adam            relu                                   16                 32                  3                 1          6.24019e-06         15        1            320.997         0.46912  │
│ trial_3d9be    TERMINATED           2   adam            tanh                                   16                 64                  3                 1          8.38585e-05         24        1            356.453         0.505585 │
│ trial_3d9be    TERMINATED           2   rmsprop         tanh                                   32                 32                  5                 1          0.000132925         20        1            184.162         0.479304 │
│ trial_3d9be    TERMINATED           3   rmsprop         relu                                   32                 16                  5                 1          1.11714e-05         18        1            270.815         0.476018 │
│ trial_3d9be    TERMINATED           3   adam            relu                                   16                 16                  5                 1          2.04657e-05         20        1            376.249         0.510184 │
│ trial_3d9be    TERMINATED           3   adam            relu                                   32                 64                  3                 1          4.31547e-05         19        1            337.404         0.560447 │
│ trial_3d9be    TERMINATED           3   rmsprop         tanh                                   32                 16                  5                 0          5.99499e-06         22        1            329.552         0.416229 │
│ trial_3d9be    TERMINATED           2   adam            tanh                                   32                 16                  3                 0          4.35707e-05         17        1            202.869         0.413929 │
╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

Mejores hiperparámetros: {'N_capas': 2, 'optimizador': 'adam', 'funcion_activacion': 'relu', 'tamanho_minilote': 16, 'numero_filtros': 64, 'tamanho_filtro': 5, 'num_resblocks': 1, 'tasa_aprendizaje': 0.00012491291538461013, 'epochs': 26}
Epoch 1/26
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762265048.386669 1899894 service.cc:152] XLA service 0x7e4eb4015f10 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762265048.386699 1899894 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-04 15:04:08.439531: 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 00:00:1762265048.757306 1899894 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762265050.883932 1899894 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 2/26

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Epoch 3/26

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Epoch 4/26

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Epoch 5/26

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Epoch 6/26

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[1m 28/665[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.5830 - loss: 0.9521  
[1m 59/665[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.5749 - loss: 0.9832
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Epoch 7/26

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Epoch 8/26

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Epoch 9/26

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Epoch 10/26

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Epoch 11/26

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Epoch 12/26

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Epoch 13/26

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Epoch 14/26

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Saved model to disk.
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[36m(train_cnn_ray_tune pid=1858809)[0m   _log_deprecation_warning(
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=== EJECUCIÓN 1 ===

--- TRAIN (ejecución 1) ---

--- TEST (ejecución 1) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Loaded model from disk.
(1545, 6, 250)
(10634, 6, 250)

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7:04[0m 1s/step
[1m 54/333[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 948us/step
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[1m54/96[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 960us/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step 
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Global accuracy score (validation) = 58.34 [%]
Global F1 score (validation) = 56.32 [%]
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[0.7085728  0.27269384 0.01739244 0.00134082]
 [0.4889408  0.38080484 0.11708368 0.0131707 ]
 [0.36434948 0.26453325 0.31713298 0.05398434]
 ...
 [0.04680704 0.02402552 0.8707262  0.05844119]
 [0.03164122 0.01318417 0.9353859  0.01978867]
 [0.03025899 0.01287114 0.93744075 0.01942915]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 75.33 [%]
Global accuracy score (test) = 60.52 [%]
Global F1 score (train) = 74.94 [%]
Global F1 score (test) = 59.36 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.46      0.33      0.39       400
MODERATE-INTENSITY       0.50      0.60      0.55       400
         SEDENTARY       0.70      0.93      0.80       400
VIGOROUS-INTENSITY       0.79      0.54      0.64       345

          accuracy                           0.61      1545
         macro avg       0.61      0.60      0.59      1545
      weighted avg       0.61      0.61      0.59      1545


2025-11-04 15:04:50.943072: 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`.
2025-11-04 15:04:50.955121: 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 00:00:1762265090.969396 1902323 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762265090.973855 1902323 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762265090.984263 1902323 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762265090.984278 1902323 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762265090.984280 1902323 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762265090.984281 1902323 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-04 15:04:50.987591: 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 00:00:1762265093.333922 1902323 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13766 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (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)
Epoch 1/26
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762265095.807428 1902450 service.cc:152] XLA service 0x7afdb4015b20 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762265095.807455 1902450 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-04 15:04:55.857289: 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 00:00:1762265096.172661 1902450 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762265098.211146 1902450 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

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
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Epoch 2/26

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Epoch 3/26

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Epoch 4/26

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Epoch 5/26

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Epoch 6/26

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Epoch 7/26

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Epoch 8/26

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Epoch 9/26

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Epoch 10/26

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Epoch 11/26

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Epoch 12/26

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Epoch 13/26

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Epoch 14/26

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Saved model to disk.
Accuracy capturado en la ejecución 1: 60.52 [%]
F1-score capturado en la ejecución 1: 59.36 [%]

=== EJECUCIÓN 2 ===

--- TRAIN (ejecución 2) ---

--- TEST (ejecución 2) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Loaded model from disk.
(1545, 6, 250)
(10634, 6, 250)

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7:08[0m 1s/step
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[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m50/96[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step 
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 11ms/step
Global accuracy score (validation) = 58.48 [%]
Global F1 score (validation) = 57.28 [%]
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[0.08313487 0.30724394 0.08470926 0.52491194]
 [0.6914686  0.1000425  0.20133398 0.00715493]
 [0.64458734 0.3099422  0.03796406 0.00750646]
 ...
 [0.15078555 0.05556602 0.63993233 0.15371609]
 [0.09202254 0.02686581 0.7869229  0.0941888 ]
 [0.11908096 0.03806856 0.7186508  0.12419964]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 78.45 [%]
Global accuracy score (test) = 59.87 [%]
Global F1 score (train) = 78.54 [%]
Global F1 score (test) = 59.25 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.40      0.34      0.37       400
MODERATE-INTENSITY       0.49      0.56      0.52       400
         SEDENTARY       0.75      0.93      0.83       400
VIGOROUS-INTENSITY       0.77      0.56      0.65       345

          accuracy                           0.60      1545
         macro avg       0.60      0.60      0.59      1545
      weighted avg       0.60      0.60      0.59      1545

2025-11-04 15:05:35.994004: 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`.
2025-11-04 15:05:36.005557: 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 00:00:1762265136.018902 1904860 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762265136.023061 1904860 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762265136.033006 1904860 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762265136.033024 1904860 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762265136.033025 1904860 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762265136.033026 1904860 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-04 15:05:36.036195: 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 00:00:1762265138.400118 1904860 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13766 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 1/26
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762265140.881088 1904990 service.cc:152] XLA service 0x7ae87c015110 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762265140.881115 1904990 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-04 15:05:40.930855: 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 00:00:1762265141.239534 1904990 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762265143.262541 1904990 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/665[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m46:15[0m 4s/step - accuracy: 0.3125 - loss: 1.9982
[1m 23/665[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.2523 - loss: 1.9298  
[1m 54/665[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.2704 - loss: 1.8400
[1m 82/665[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.2883 - loss: 1.7966
[1m114/665[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3044 - loss: 1.7589
[1m146/665[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3159 - loss: 1.7302
[1m178/665[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3248 - loss: 1.7086
[1m207/665[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3314 - loss: 1.6922
[1m234/665[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3370 - loss: 1.6777
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Epoch 2/26

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Epoch 3/26

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Epoch 4/26

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Epoch 5/26

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Epoch 6/26

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Epoch 7/26

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Epoch 8/26

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Epoch 9/26

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Epoch 10/26

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Epoch 11/26

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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m51s[0m 1s/step2025-11-04 15:06:04.953853: 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.

Accuracy capturado en la ejecución 2: 59.87 [%]
F1-score capturado en la ejecución 2: 59.25 [%]

=== EJECUCIÓN 3 ===

--- TRAIN (ejecución 3) ---

--- TEST (ejecución 3) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Loaded model from disk.
(1545, 6, 250)
(10634, 6, 250)

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7:07[0m 1s/step
[1m 52/333[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 992us/step
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[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 17ms/step
[1m54/96[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 945us/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step 
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Global accuracy score (validation) = 58.44 [%]
Global F1 score (validation) = 55.57 [%]
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[0.4514423  0.48612645 0.03931495 0.02311628]
 [0.27156195 0.26372686 0.33287552 0.13183565]
 [0.36099526 0.5474182  0.08809987 0.00348664]
 ...
 [0.1028213  0.02542529 0.80873024 0.06302308]
 [0.1092592  0.02496445 0.78134376 0.08443264]
 [0.11355763 0.02905487 0.78518677 0.07220075]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 69.43 [%]
Global accuracy score (test) = 59.09 [%]
Global F1 score (train) = 67.9 [%]
Global F1 score (test) = 57.0 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.41      0.21      0.28       400
MODERATE-INTENSITY       0.47      0.69      0.56       400
         SEDENTARY       0.72      0.91      0.80       400
VIGOROUS-INTENSITY       0.78      0.54      0.64       345

          accuracy                           0.59      1545
         macro avg       0.59      0.59      0.57      1545
      weighted avg       0.59      0.59      0.57      1545


Accuracy capturado en la ejecución 3: 59.09 [%]
F1-score capturado en la ejecución 3: 57.0 [%]
2025-11-04 15:06:16.899541: 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`.
2025-11-04 15:06:16.911345: 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 00:00:1762265176.925397 1907080 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762265176.929834 1907080 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762265176.940252 1907080 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762265176.940270 1907080 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762265176.940272 1907080 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762265176.940273 1907080 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-04 15:06:16.943612: 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 00:00:1762265179.292620 1907080 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13766 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)
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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)
Epoch 1/26
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762265181.813216 1907208 service.cc:152] XLA service 0x79b6ac028b40 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762265181.813263 1907208 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-04 15:06:21.870823: 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 00:00:1762265182.189404 1907208 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762265184.190274 1907208 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

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
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Epoch 2/26

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Epoch 3/26

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Epoch 4/26

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Epoch 5/26

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Epoch 6/26

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Epoch 7/26

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Epoch 8/26

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Epoch 9/26

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Epoch 10/26

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Epoch 11/26

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Epoch 12/26

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Saved model to disk.

=== EJECUCIÓN 4 ===

--- TRAIN (ejecución 4) ---

--- TEST (ejecución 4) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Loaded model from disk.
(1545, 6, 250)
(10634, 6, 250)

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7:19[0m 1s/step
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[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m57/96[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 900us/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step 
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Global accuracy score (validation) = 59.17 [%]
Global F1 score (validation) = 56.25 [%]
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[0.18694484 0.37844196 0.13359486 0.30101842]
 [0.1758506  0.50098866 0.0652796  0.2578811 ]
 [0.28704754 0.23778236 0.3162557  0.15891445]
 ...
 [0.08098234 0.01739569 0.84068096 0.06094103]
 [0.06181997 0.01061146 0.8843495  0.04321903]
 [0.06826615 0.0128517  0.8655459  0.0533362 ]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 72.62 [%]
Global accuracy score (test) = 57.67 [%]
Global F1 score (train) = 71.39 [%]
Global F1 score (test) = 54.99 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.40      0.20      0.26       400
MODERATE-INTENSITY       0.47      0.66      0.55       400
         SEDENTARY       0.68      0.94      0.79       400
VIGOROUS-INTENSITY       0.75      0.51      0.61       345

          accuracy                           0.58      1545
         macro avg       0.57      0.57      0.55      1545
      weighted avg       0.57      0.58      0.55      1545


2025-11-04 15:06:59.233942: 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`.
2025-11-04 15:06:59.245275: 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 00:00:1762265219.258397 1909415 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762265219.262468 1909415 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762265219.272276 1909415 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762265219.272293 1909415 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762265219.272295 1909415 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762265219.272297 1909415 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-04 15:06:59.275528: 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 00:00:1762265221.633166 1909415 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13766 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 1/26
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762265224.170833 1909518 service.cc:152] XLA service 0x70127c014630 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762265224.170867 1909518 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-04 15:07:04.225118: 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 00:00:1762265224.537277 1909518 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762265226.572780 1909518 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/665[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m47:15[0m 4s/step - accuracy: 0.1875 - loss: 1.8943
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[1m 56/665[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.2925 - loss: 1.7626
[1m 90/665[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3110 - loss: 1.7300
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Epoch 2/26

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Epoch 3/26

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Epoch 4/26

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Epoch 5/26

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Epoch 6/26

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Epoch 7/26

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Epoch 8/26

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Epoch 9/26

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Epoch 10/26

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Epoch 11/26

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Epoch 12/26

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Epoch 13/26

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Epoch 14/26

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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m51s[0m 1s/step2025-11-04 15:07:32.236820: 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 22ms/step
Saved model to disk.
Accuracy capturado en la ejecución 4: 57.67 [%]
F1-score capturado en la ejecución 4: 54.99 [%]

=== EJECUCIÓN 5 ===

--- TRAIN (ejecución 5) ---

--- TEST (ejecución 5) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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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, 6, 250)
(10634, 6, 250)

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[1m111/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 919us/step
[1m165/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 922us/step
[1m224/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 902us/step
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[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step  
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[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 18ms/step

[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 17ms/step
[1m52/96[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 985us/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step 
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 11ms/step
Global accuracy score (validation) = 58.71 [%]
Global F1 score (validation) = 58.69 [%]
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[0.4204961  0.45800766 0.07795035 0.04354597]
 [0.57678616 0.37039596 0.04093921 0.01187865]
 [0.27645302 0.6611334  0.02382662 0.038587  ]
 ...
 [0.2514798  0.14054924 0.41621143 0.19175954]
 [0.17672914 0.07504038 0.64475524 0.10347523]
 [0.18123911 0.07822543 0.6351328  0.10540264]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 77.21 [%]
Global accuracy score (test) = 59.03 [%]
Global F1 score (train) = 77.91 [%]
Global F1 score (test) = 59.61 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.41      0.55      0.47       400
MODERATE-INTENSITY       0.52      0.49      0.50       400
         SEDENTARY       0.80      0.81      0.80       400
VIGOROUS-INTENSITY       0.78      0.50      0.61       345

          accuracy                           0.59      1545
         macro avg       0.63      0.59      0.60      1545
      weighted avg       0.62      0.59      0.60      1545


Accuracy capturado en la ejecución 5: 59.03 [%]
2025-11-04 15:07:44.239284: 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`.
2025-11-04 15:07:44.250633: 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 00:00:1762265264.263565 1911941 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762265264.267506 1911941 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762265264.277684 1911941 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762265264.277701 1911941 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762265264.277702 1911941 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762265264.277704 1911941 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-04 15:07:44.280907: 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 00:00:1762265266.604658 1911941 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13766 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 1/26
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762265269.089335 1912057 service.cc:152] XLA service 0x797494007260 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762265269.089381 1912057 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-04 15:07:49.145261: 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 00:00:1762265269.453121 1912057 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762265271.467839 1912057 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/665[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m46:31[0m 4s/step - accuracy: 0.3125 - loss: 1.7740
[1m 24/665[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.2809 - loss: 1.7310  
[1m 54/665[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.2996 - loss: 1.6843
[1m 84/665[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.3189 - loss: 1.6530
[1m116/665[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3352 - loss: 1.6308
[1m145/665[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3454 - loss: 1.6153
[1m176/665[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3534 - loss: 1.6025
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[1m236/665[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3629 - loss: 1.5829
[1m267/665[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3664 - loss: 1.5758
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Epoch 2/26

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Epoch 3/26

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Epoch 4/26

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Epoch 5/26

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Epoch 6/26

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Epoch 7/26

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Epoch 8/26

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Epoch 9/26

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Epoch 10/26

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Epoch 11/26

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Epoch 12/26

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Epoch 13/26

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Epoch 14/26

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Epoch 15/26

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Epoch 16/26

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Epoch 17/26

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Saved model to disk.
F1-score capturado en la ejecución 5: 59.61 [%]

=== EJECUCIÓN 6 ===

--- TRAIN (ejecución 6) ---

--- TEST (ejecución 6) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Loaded model from disk.
(1545, 6, 250)
(10634, 6, 250)

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7:07[0m 1s/step
[1m 57/333[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 896us/step
[1m117/333[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 866us/step
[1m173/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 877us/step
[1m234/333[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 864us/step
[1m292/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 867us/step
[1m333/333[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 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m56/96[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 915us/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step 
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 11ms/step
Global accuracy score (validation) = 58.67 [%]
Global F1 score (validation) = 57.46 [%]
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[5.1265043e-01 3.2224083e-01 5.4238643e-02 1.1087012e-01]
 [4.9033341e-01 2.3540801e-01 2.4082001e-01 3.3438664e-02]
 [7.1030244e-02 9.2426580e-01 4.2241574e-03 4.7969923e-04]
 ...
 [4.2987701e-02 7.4993302e-03 9.3018693e-01 1.9325994e-02]
 [1.7865050e-01 3.9281093e-02 7.4329066e-01 3.8777847e-02]
 [6.0267530e-02 1.0850365e-02 9.0706277e-01 2.1819336e-02]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 78.03 [%]
Global accuracy score (test) = 57.09 [%]
Global F1 score (train) = 78.15 [%]
Global F1 score (test) = 56.54 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.37      0.33      0.35       400
MODERATE-INTENSITY       0.46      0.50      0.48       400
         SEDENTARY       0.71      0.91      0.80       400
VIGOROUS-INTENSITY       0.79      0.54      0.64       345

          accuracy                           0.57      1545
         macro avg       0.58      0.57      0.57      1545
      weighted avg       0.57      0.57      0.56      1545


2025-11-04 15:08:33.757490: 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`.
2025-11-04 15:08:33.768505: 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 00:00:1762265313.781245 1914799 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762265313.785126 1914799 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762265313.795452 1914799 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762265313.795469 1914799 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762265313.795471 1914799 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762265313.795472 1914799 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-04 15:08:33.798574: 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 00:00:1762265316.144230 1914799 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13768 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 1/26
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762265318.623294 1914921 service.cc:152] XLA service 0x72a344027380 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762265318.623317 1914921 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-04 15:08:38.673157: 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 00:00:1762265318.986161 1914921 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762265321.010802 1914921 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/665[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m46:31[0m 4s/step - accuracy: 0.1875 - loss: 2.1304
[1m 24/665[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.2341 - loss: 2.0134  
[1m 54/665[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.2730 - loss: 1.9297
[1m 80/665[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.2903 - loss: 1.8835
[1m109/665[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.3019 - loss: 1.8456
[1m137/665[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3106 - loss: 1.8165
[1m167/665[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3191 - loss: 1.7909
[1m197/665[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3272 - loss: 1.7682
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[1m286/665[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3451 - loss: 1.7205
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[1m347/665[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3542 - loss: 1.6958
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Epoch 2/26

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Epoch 3/26

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Epoch 4/26

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Epoch 5/26

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Epoch 6/26

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Epoch 7/26

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Epoch 8/26

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Epoch 9/26

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Epoch 10/26

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Epoch 11/26

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Epoch 12/26

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Epoch 13/26

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Epoch 14/26

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Epoch 15/26

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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m52s[0m 1s/step2025-11-04 15:09:08.720446: 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.
Accuracy capturado en la ejecución 6: 57.09 [%]
F1-score capturado en la ejecución 6: 56.54 [%]

=== EJECUCIÓN 7 ===

--- TRAIN (ejecución 7) ---

--- TEST (ejecución 7) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Loaded model from disk.
(1545, 6, 250)
(10634, 6, 250)

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7:18[0m 1s/step
[1m 53/333[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 969us/step
[1m108/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 940us/step
[1m168/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 906us/step
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[1m283/333[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 894us/step
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[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 18ms/step
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[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 17ms/step
[1m53/96[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 972us/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step 
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Global accuracy score (validation) = 57.98 [%]
Global F1 score (validation) = 56.71 [%]
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[0.563791   0.29531968 0.0756447  0.06524459]
 [0.63425523 0.2207018  0.07041056 0.07463241]
 [0.36123258 0.4041394  0.10084441 0.13378367]
 ...
 [0.01976502 0.00466104 0.94285804 0.03271598]
 [0.01708528 0.00355268 0.9574253  0.02193675]
 [0.0598602  0.01557639 0.8724482  0.05211523]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 77.22 [%]
Global accuracy score (test) = 59.42 [%]
Global F1 score (train) = 77.32 [%]
Global F1 score (test) = 58.84 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.44      0.42      0.43       400
MODERATE-INTENSITY       0.48      0.50      0.49       400
         SEDENTARY       0.72      0.91      0.80       400
VIGOROUS-INTENSITY       0.75      0.54      0.63       345

          accuracy                           0.59      1545
         macro avg       0.60      0.59      0.59      1545
      weighted avg       0.59      0.59      0.59      1545


Accuracy capturado en la ejecución 7: 59.42 [%]
2025-11-04 15:09:20.821561: 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`.
2025-11-04 15:09:20.832642: 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 00:00:1762265360.845676 1917470 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762265360.849752 1917470 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762265360.859462 1917470 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762265360.859477 1917470 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762265360.859478 1917470 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762265360.859480 1917470 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-04 15:09:20.862636: 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 00:00:1762265363.226674 1917470 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13766 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (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)
Epoch 1/26
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762265365.699539 1917587 service.cc:152] XLA service 0x7bbc28014a50 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762265365.699565 1917587 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-04 15:09:25.748992: 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 00:00:1762265366.057684 1917587 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762265368.056004 1917587 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

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
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Epoch 2/26

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Epoch 3/26

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Epoch 4/26

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Epoch 5/26

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Epoch 6/26

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Epoch 7/26

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Epoch 8/26

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Epoch 9/26

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Epoch 10/26

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Epoch 11/26

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Epoch 12/26

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Epoch 13/26

[1m  1/665[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 21ms/step - accuracy: 0.8125 - loss: 0.6143
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[1m 84/665[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.6974 - loss: 0.7361
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[1m231/665[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.7007 - loss: 0.7222
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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m52s[0m 1s/step2025-11-04 15:09:52.604750: 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 20ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 21ms/step
Saved model to disk.
F1-score capturado en la ejecución 7: 58.84 [%]

=== EJECUCIÓN 8 ===

--- TRAIN (ejecución 8) ---

--- TEST (ejecución 8) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
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Loaded model from disk.
(1545, 6, 250)
(10634, 6, 250)

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7:09[0m 1s/step
[1m 49/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step 
[1m105/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 963us/step
[1m168/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 901us/step
[1m226/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 891us/step
[1m287/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 878us/step
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step  
[1m333/333[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 19ms/step

[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m60/96[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 857us/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step 
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 11ms/step
Global accuracy score (validation) = 59.1 [%]
Global F1 score (validation) = 57.17 [%]
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[0.6660588  0.3239425  0.00810244 0.00189632]
 [0.56480926 0.35505676 0.0588198  0.02131414]
 [0.4276616  0.28934282 0.15898544 0.12401013]
 ...
 [0.03703853 0.00563972 0.91666937 0.04065245]
 [0.03710329 0.00565407 0.91653055 0.04071212]
 [0.04209524 0.00671286 0.9010746  0.05011727]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 73.81 [%]
Global accuracy score (test) = 58.38 [%]
Global F1 score (train) = 73.38 [%]
Global F1 score (test) = 57.18 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.42      0.32      0.36       400
MODERATE-INTENSITY       0.47      0.54      0.50       400
         SEDENTARY       0.71      0.94      0.81       400
VIGOROUS-INTENSITY       0.72      0.54      0.62       345

          accuracy                           0.58      1545
         macro avg       0.58      0.58      0.57      1545
      weighted avg       0.57      0.58      0.57      1545


Accuracy capturado en la ejecución 8: 58.38 [%]
F1-score capturado en la ejecución 8: 57.18 [%]2025-11-04 15:10:04.584663: 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`.
2025-11-04 15:10:04.596725: 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 00:00:1762265404.610772 1919894 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762265404.615000 1919894 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762265404.625389 1919894 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762265404.625405 1919894 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762265404.625407 1919894 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762265404.625409 1919894 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-04 15:10:04.628559: 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 00:00:1762265406.955050 1919894 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13766 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 1/26
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762265409.446255 1920026 service.cc:152] XLA service 0x77fa3c002b30 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762265409.446286 1920026 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-04 15:10:09.499462: 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 00:00:1762265409.818180 1920026 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762265411.834132 1920026 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 2/26

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Epoch 3/26

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Epoch 4/26

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Epoch 5/26

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Epoch 6/26

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Epoch 7/26

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Epoch 8/26

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Epoch 9/26

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Epoch 10/26

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Epoch 11/26

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Epoch 12/26

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Epoch 13/26

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Epoch 14/26

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Saved model to disk.


=== EJECUCIÓN 9 ===

--- TRAIN (ejecución 9) ---

--- TEST (ejecución 9) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Loaded model from disk.
(1545, 6, 250)
(10634, 6, 250)

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7:11[0m 1s/step
[1m 48/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step 
[1m107/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 957us/step
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[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 17ms/step
[1m56/96[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 917us/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step 
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Global accuracy score (validation) = 59.07 [%]
Global F1 score (validation) = 57.39 [%]
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[0.42168278 0.20570901 0.28057358 0.09203461]
 [0.16297863 0.1017116  0.37557536 0.3597344 ]
 [0.4878101  0.42882043 0.06341777 0.01995172]
 ...
 [0.12958428 0.03476378 0.7891757  0.04647616]
 [0.12236291 0.03230305 0.8050288  0.04030525]
 [0.22623217 0.09098122 0.59897697 0.08380958]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 76.8 [%]
Global accuracy score (test) = 58.83 [%]
Global F1 score (train) = 76.64 [%]
Global F1 score (test) = 57.91 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.41      0.32      0.36       400
MODERATE-INTENSITY       0.48      0.60      0.53       400
         SEDENTARY       0.73      0.91      0.81       400
VIGOROUS-INTENSITY       0.77      0.52      0.62       345

          accuracy                           0.59      1545
         macro avg       0.60      0.59      0.58      1545
      weighted avg       0.59      0.59      0.58      1545


2025-11-04 15:10:49.424936: 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`.
2025-11-04 15:10:49.436417: 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 00:00:1762265449.449794 1922423 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762265449.453810 1922423 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762265449.464107 1922423 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762265449.464126 1922423 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762265449.464127 1922423 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762265449.464129 1922423 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-04 15:10:49.467249: 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 00:00:1762265451.801790 1922423 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13766 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 1/26
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762265454.308382 1922559 service.cc:152] XLA service 0x791fa8017840 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762265454.308408 1922559 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-04 15:10:54.358162: 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 00:00:1762265454.680541 1922559 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762265456.715585 1922559 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/665[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m46:58[0m 4s/step - accuracy: 0.2500 - loss: 1.7024
[1m 26/665[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.2392 - loss: 1.9844  
[1m 57/665[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.2564 - loss: 1.9272
[1m 86/665[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.2754 - loss: 1.8769
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
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Epoch 2/26

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Epoch 3/26

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Epoch 4/26

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Epoch 5/26

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Epoch 6/26

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Epoch 7/26

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Epoch 8/26

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Epoch 9/26

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Epoch 10/26

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Epoch 11/26

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Epoch 12/26

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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m52s[0m 1s/step2025-11-04 15:11:19.911713: 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.
Accuracy capturado en la ejecución 9: 58.83 [%]
F1-score capturado en la ejecución 9: 57.91 [%]

=== EJECUCIÓN 10 ===

--- TRAIN (ejecución 10) ---

--- TEST (ejecución 10) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (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, 6, 250)
(10634, 6, 250)

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7:06[0m 1s/step
[1m 56/333[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 919us/step
[1m117/333[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 869us/step
[1m178/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 856us/step
[1m239/333[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 848us/step
[1m300/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 844us/step
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step  
[1m333/333[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 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m55/96[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 937us/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step 
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 11ms/step
Global accuracy score (validation) = 58.64 [%]
Global F1 score (validation) = 56.45 [%]
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[0.3726166  0.27893534 0.23783368 0.11061433]
 [0.35634503 0.59144044 0.03860229 0.01361221]
 [0.36893338 0.37268612 0.16761062 0.09076988]
 ...
 [0.08873736 0.01093417 0.8579167  0.04241186]
 [0.06190233 0.00548529 0.91109705 0.02151537]
 [0.06325853 0.00571748 0.9085465  0.02247747]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 73.81 [%]
Global accuracy score (test) = 60.13 [%]
Global F1 score (train) = 73.19 [%]
Global F1 score (test) = 58.96 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.41      0.32      0.35       400
MODERATE-INTENSITY       0.52      0.62      0.56       400
         SEDENTARY       0.72      0.93      0.81       400
VIGOROUS-INTENSITY       0.77      0.53      0.63       345

          accuracy                           0.60      1545
         macro avg       0.60      0.60      0.59      1545
      weighted avg       0.60      0.60      0.59      1545


Accuracy capturado en la ejecución 10: 60.13 [%]
2025-11-04 15:11:31.995415: 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`.
2025-11-04 15:11:32.007379: 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 00:00:1762265492.021573 1924771 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762265492.026022 1924771 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762265492.036634 1924771 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762265492.036653 1924771 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762265492.036654 1924771 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762265492.036663 1924771 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-04 15:11:32.040029: 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 00:00:1762265494.386549 1924771 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13766 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 1/26
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762265496.891915 1924880 service.cc:152] XLA service 0x7cbb5c0189b0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762265496.891945 1924880 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-04 15:11:36.943094: 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 00:00:1762265497.255236 1924880 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762265499.267726 1924880 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/665[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m46:34[0m 4s/step - accuracy: 0.1875 - loss: 1.8294
[1m 26/665[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.2434 - loss: 1.8751  
[1m 56/665[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.2803 - loss: 1.7985
[1m 86/665[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.3028 - loss: 1.7488
[1m116/665[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3180 - loss: 1.7145
[1m147/665[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3289 - loss: 1.6876
[1m178/665[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3368 - loss: 1.6680
[1m207/665[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3422 - loss: 1.6536
[1m236/665[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3464 - loss: 1.6411
[1m267/665[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3501 - loss: 1.6295
[1m293/665[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3529 - loss: 1.6205
[1m324/665[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3561 - loss: 1.6099
[1m356/665[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3593 - loss: 1.5997
[1m385/665[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3618 - loss: 1.5917
[1m415/665[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3642 - loss: 1.5839
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Epoch 2/26

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Epoch 3/26

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Epoch 4/26

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Epoch 5/26

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Epoch 6/26

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Epoch 7/26

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Epoch 8/26

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Epoch 9/26

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Epoch 10/26

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Epoch 11/26

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Epoch 12/26

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Epoch 13/26

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Epoch 14/26

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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m51s[0m 1s/step2025-11-04 15:12:05.357328: 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.
F1-score capturado en la ejecución 10: 58.96 [%]

=== EJECUCIÓN 11 ===

--- TRAIN (ejecución 11) ---

--- TEST (ejecución 11) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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Loaded model from disk.
(1545, 6, 250)
(10634, 6, 250)

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[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 18ms/step
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[1m51/96[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step 
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 11ms/step
Global accuracy score (validation) = 57.82 [%]
Global F1 score (validation) = 57.3 [%]
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[0.44720286 0.40687436 0.08841009 0.05751264]
 [0.684327   0.292936   0.02091046 0.00182656]
 [0.33336347 0.6274368  0.0265728  0.01262691]
 ...
 [0.0279462  0.0040508  0.9564334  0.01156957]
 [0.05901662 0.0110749  0.90574265 0.0241659 ]
 [0.07888192 0.01672855 0.87555224 0.02883737]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 77.21 [%]
Global accuracy score (test) = 60.97 [%]
Global F1 score (train) = 77.41 [%]
Global F1 score (test) = 60.75 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.45      0.53      0.49       400
MODERATE-INTENSITY       0.50      0.46      0.48       400
         SEDENTARY       0.78      0.91      0.84       400
VIGOROUS-INTENSITY       0.76      0.54      0.63       345

          accuracy                           0.61      1545
         macro avg       0.62      0.61      0.61      1545
      weighted avg       0.62      0.61      0.61      1545


Accuracy capturado en la ejecución 11: 60.97 [%]
F1-score capturado en la ejecución 11: 60.75 [%]
2025-11-04 15:12:17.442517: 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`.
2025-11-04 15:12:17.454030: 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 00:00:1762265537.467359 1927307 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762265537.471386 1927307 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762265537.481507 1927307 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762265537.481522 1927307 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762265537.481524 1927307 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762265537.481525 1927307 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-04 15:12:17.484652: 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 00:00:1762265539.830357 1927307 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13766 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 1/26
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762265542.306013 1927416 service.cc:152] XLA service 0x77aff4001530 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762265542.306039 1927416 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-04 15:12:22.356172: 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 00:00:1762265542.663979 1927416 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762265544.653977 1927416 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/665[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m46:01[0m 4s/step - accuracy: 0.1250 - loss: 1.8805
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[1m 88/665[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.2889 - loss: 1.7807
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Epoch 2/26

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Epoch 3/26

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Epoch 4/26

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Epoch 5/26

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Epoch 6/26

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Epoch 7/26

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Epoch 8/26

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Epoch 9/26

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Epoch 10/26

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Epoch 11/26

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Epoch 12/26

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Epoch 13/26

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Saved model to disk.

=== EJECUCIÓN 12 ===

--- TRAIN (ejecución 12) ---

--- TEST (ejecución 12) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Loaded model from disk.
(1545, 6, 250)
(10634, 6, 250)

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6:56[0m 1s/step
[1m 47/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step 
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[1m48/96[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step 
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Global accuracy score (validation) = 59.59 [%]
Global F1 score (validation) = 57.47 [%]
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[0.53715235 0.31927577 0.12803756 0.01553439]
 [0.6417115  0.35401747 0.00248388 0.00178718]
 [0.18788564 0.79540056 0.00950223 0.00721156]
 ...
 [0.04159204 0.00967694 0.9379145  0.01081652]
 [0.06551185 0.01674267 0.89661425 0.02113125]
 [0.05709105 0.01368567 0.91210306 0.01712023]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 73.67 [%]
Global accuracy score (test) = 59.81 [%]
Global F1 score (train) = 73.13 [%]
Global F1 score (test) = 58.27 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.44      0.30      0.36       400
MODERATE-INTENSITY       0.50      0.62      0.56       400
         SEDENTARY       0.70      0.94      0.80       400
VIGOROUS-INTENSITY       0.76      0.52      0.62       345

          accuracy                           0.60      1545
         macro avg       0.60      0.60      0.58      1545
      weighted avg       0.59      0.60      0.58      1545


2025-11-04 15:13:01.284338: 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`.
2025-11-04 15:13:01.295695: 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 00:00:1762265581.308817 1929719 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762265581.312932 1929719 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762265581.322747 1929719 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762265581.322764 1929719 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762265581.322766 1929719 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762265581.322767 1929719 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-04 15:13:01.325869: 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 00:00:1762265583.708754 1929719 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13766 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 1/26
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762265586.228118 1929857 service.cc:152] XLA service 0x7bb48c002e30 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762265586.228146 1929857 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-04 15:13:06.278099: 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 00:00:1762265586.587000 1929857 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762265588.608072 1929857 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/665[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m46:53[0m 4s/step - accuracy: 0.2500 - loss: 1.9492
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[1m 56/665[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.2748 - loss: 1.8814
[1m 83/665[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.2925 - loss: 1.8381
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Epoch 2/26

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Epoch 3/26

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Epoch 4/26

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Epoch 5/26

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Epoch 6/26

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Epoch 7/26

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Epoch 8/26

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Epoch 9/26

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Epoch 10/26

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Epoch 11/26

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Epoch 12/26

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Epoch 13/26

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Epoch 14/26

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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m52s[0m 1s/step2025-11-04 15:13:34.184502: 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.
Accuracy capturado en la ejecución 12: 59.81 [%]
F1-score capturado en la ejecución 12: 58.27 [%]

=== EJECUCIÓN 13 ===

--- TRAIN (ejecución 13) ---

--- TEST (ejecución 13) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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Loaded model from disk.
(1545, 6, 250)
(10634, 6, 250)

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[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 18ms/step
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[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m52/96[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 995us/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step 
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 11ms/step
Global accuracy score (validation) = 59.43 [%]
Global F1 score (validation) = 58.13 [%]
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[0.36967632 0.13189568 0.29982764 0.1986004 ]
 [0.06903808 0.081062   0.07635158 0.7735484 ]
 [0.47911343 0.38088557 0.10615289 0.03384813]
 ...
 [0.03912808 0.006461   0.94193894 0.01247196]
 [0.11499222 0.02952664 0.8267142  0.02876691]
 [0.05541204 0.01049764 0.91587377 0.01821652]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 74.78 [%]
Global accuracy score (test) = 59.35 [%]
Global F1 score (train) = 74.65 [%]
Global F1 score (test) = 58.32 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.45      0.35      0.39       400
MODERATE-INTENSITY       0.49      0.52      0.50       400
         SEDENTARY       0.69      0.92      0.79       400
VIGOROUS-INTENSITY       0.72      0.59      0.65       345

          accuracy                           0.59      1545
         macro avg       0.59      0.59      0.58      1545
      weighted avg       0.58      0.59      0.58      1545

2025-11-04 15:13:46.213048: 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`.
2025-11-04 15:13:46.224298: 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 00:00:1762265626.237452 1932259 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762265626.241663 1932259 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762265626.251735 1932259 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762265626.251754 1932259 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762265626.251755 1932259 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762265626.251756 1932259 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-04 15:13:46.255056: 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 00:00:1762265628.615259 1932259 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13768 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 1/26
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762265631.104169 1932391 service.cc:152] XLA service 0x7f23a4016110 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762265631.104194 1932391 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-04 15:13:51.153720: 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 00:00:1762265631.466711 1932391 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762265633.526217 1932391 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/665[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m46:52[0m 4s/step - accuracy: 0.3125 - loss: 1.8907
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[1m 50/665[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.2691 - loss: 1.8655
[1m 81/665[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.2820 - loss: 1.8255
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[1m292/665[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3409 - loss: 1.6866
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[1m355/665[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3504 - loss: 1.6637
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Epoch 2/26

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Epoch 3/26

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Epoch 4/26

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Epoch 5/26

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Epoch 6/26

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Epoch 7/26

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Epoch 8/26

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Epoch 9/26

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Epoch 10/26

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Epoch 11/26

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Epoch 12/26

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Epoch 13/26

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Epoch 14/26

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Epoch 15/26

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Epoch 16/26

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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m50s[0m 1s/step2025-11-04 15:14:22.511696: 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.

Accuracy capturado en la ejecución 13: 59.35 [%]
F1-score capturado en la ejecución 13: 58.32 [%]

=== EJECUCIÓN 14 ===

--- TRAIN (ejecución 14) ---

--- TEST (ejecución 14) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Loaded model from disk.
(1545, 6, 250)
(10634, 6, 250)

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7:33[0m 1s/step
[1m 59/333[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 872us/step
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[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 17ms/step
[1m56/96[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 923us/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step 
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Global accuracy score (validation) = 57.19 [%]
Global F1 score (validation) = 57.75 [%]
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[0.41760218 0.5557543  0.01938908 0.00725438]
 [0.30539015 0.657824   0.01500691 0.02177898]
 [0.02467558 0.9351243  0.00829099 0.03190916]
 ...
 [0.20128186 0.08161297 0.57171637 0.1453888 ]
 [0.14989646 0.05130099 0.7372124  0.06159018]
 [0.21569777 0.08801546 0.5784515  0.11783522]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 78.63 [%]
Global accuracy score (test) = 57.22 [%]
Global F1 score (train) = 79.3 [%]
Global F1 score (test) = 57.96 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.40      0.43      0.41       400
MODERATE-INTENSITY       0.47      0.66      0.55       400
         SEDENTARY       0.83      0.72      0.77       400
VIGOROUS-INTENSITY       0.81      0.46      0.59       345

          accuracy                           0.57      1545
         macro avg       0.63      0.57      0.58      1545
      weighted avg       0.62      0.57      0.58      1545


Accuracy capturado en la ejecución 14: 57.22 [%]
2025-11-04 15:14:34.555315: 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`.
2025-11-04 15:14:34.566711: 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 00:00:1762265674.579732 1935028 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762265674.583814 1935028 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762265674.593702 1935028 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762265674.593717 1935028 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762265674.593718 1935028 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762265674.593720 1935028 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-04 15:14:34.596829: 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 00:00:1762265676.944514 1935028 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13768 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 1/26
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762265679.449880 1935139 service.cc:152] XLA service 0x73340c002800 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762265679.449907 1935139 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-04 15:14:39.500260: 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 00:00:1762265679.809310 1935139 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762265681.809818 1935139 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 2/26

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Epoch 3/26

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Epoch 4/26

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Epoch 5/26

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Epoch 6/26

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Epoch 7/26

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Epoch 8/26

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Epoch 9/26

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Epoch 10/26

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Epoch 11/26

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Epoch 12/26

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Saved model to disk.
F1-score capturado en la ejecución 14: 57.96 [%]

=== EJECUCIÓN 15 ===

--- TRAIN (ejecución 15) ---

--- TEST (ejecución 15) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Loaded model from disk.
(1545, 6, 250)
(10634, 6, 250)

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7:28[0m 1s/step
[1m 49/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step 
[1m107/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 954us/step
[1m166/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 920us/step
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[1m274/333[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 925us/step
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[1m50/96[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step 
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 11ms/step
Global accuracy score (validation) = 57.92 [%]
Global F1 score (validation) = 56.82 [%]
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[0.46686587 0.499116   0.02628157 0.00773654]
 [0.5345719  0.4080611  0.03943599 0.01793105]
 [0.8191342  0.11224065 0.0358421  0.03278307]
 ...
 [0.18800671 0.05017003 0.66609895 0.09572428]
 [0.09526053 0.01519557 0.84580505 0.04373893]
 [0.12375095 0.0229892  0.79199845 0.06126132]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 72.68 [%]
Global accuracy score (test) = 58.38 [%]
Global F1 score (train) = 72.55 [%]
Global F1 score (test) = 57.64 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.42      0.41      0.42       400
MODERATE-INTENSITY       0.49      0.43      0.46       400
         SEDENTARY       0.70      0.92      0.79       400
VIGOROUS-INTENSITY       0.73      0.57      0.64       345

          accuracy                           0.58      1545
         macro avg       0.58      0.58      0.58      1545
      weighted avg       0.58      0.58      0.57      1545

2025-11-04 15:15:16.974832: 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`.
2025-11-04 15:15:16.985947: 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 00:00:1762265716.998956 1937346 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762265717.003261 1937346 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762265717.013328 1937346 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762265717.013344 1937346 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762265717.013345 1937346 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762265717.013346 1937346 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-04 15:15:17.016608: 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 00:00:1762265719.367245 1937346 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13768 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 1/26
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762265721.884684 1937479 service.cc:152] XLA service 0x7aa0ec0134b0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762265721.884722 1937479 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-04 15:15:21.939578: 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 00:00:1762265722.249931 1937479 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762265724.316326 1937479 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/665[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m47:13[0m 4s/step - accuracy: 0.2500 - loss: 2.2108
[1m 26/665[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.2990 - loss: 1.9186  
[1m 55/665[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.3052 - loss: 1.8538
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
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Epoch 2/26

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Epoch 3/26

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Epoch 4/26

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Epoch 5/26

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Epoch 6/26

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Epoch 7/26

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Epoch 8/26

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Epoch 9/26

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Epoch 10/26

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Epoch 11/26

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Epoch 12/26

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Saved model to disk.

Accuracy capturado en la ejecución 15: 58.38 [%]
F1-score capturado en la ejecución 15: 57.64 [%]

=== EJECUCIÓN 16 ===

--- TRAIN (ejecución 16) ---

--- TEST (ejecución 16) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Loaded model from disk.
(1545, 6, 250)
(10634, 6, 250)

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7:16[0m 1s/step
[1m 51/333[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step 
[1m105/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 967us/step
[1m161/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 946us/step
[1m217/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 933us/step
[1m274/333[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 925us/step
[1m330/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 921us/step
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step  
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[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 18ms/step
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[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m52/96[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 995us/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step 
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 10ms/step
Global accuracy score (validation) = 56.8 [%]
Global F1 score (validation) = 55.48 [%]
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[0.40746132 0.5784116  0.01102419 0.003103  ]
 [0.6440663  0.3231299  0.02257242 0.01023151]
 [0.45972154 0.2530264  0.05449998 0.2327521 ]
 ...
 [0.1639935  0.0406184  0.72372854 0.07165955]
 [0.12404781 0.02532051 0.7960181  0.0546135 ]
 [0.18461648 0.05055687 0.68852586 0.07630084]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 72.16 [%]
Global accuracy score (test) = 59.22 [%]
Global F1 score (train) = 72.03 [%]
Global F1 score (test) = 58.74 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.40      0.39      0.40       400
MODERATE-INTENSITY       0.50      0.47      0.49       400
         SEDENTARY       0.74      0.91      0.81       400
VIGOROUS-INTENSITY       0.72      0.59      0.65       345

          accuracy                           0.59      1545
         macro avg       0.59      0.59      0.59      1545
      weighted avg       0.59      0.59      0.59      1545

2025-11-04 15:15:59.656231: 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`.
2025-11-04 15:15:59.667328: 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 00:00:1762265759.680597 1939694 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762265759.685007 1939694 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762265759.695407 1939694 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762265759.695426 1939694 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762265759.695427 1939694 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762265759.695429 1939694 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-04 15:15:59.698778: 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 00:00:1762265762.061176 1939694 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13768 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 1/26
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762265764.593282 1939805 service.cc:152] XLA service 0x7d3360017d00 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762265764.593310 1939805 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-04 15:16:04.643589: 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 00:00:1762265764.969804 1939805 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762265767.000094 1939805 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/665[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m47:06[0m 4s/step - accuracy: 0.3125 - loss: 1.6682
[1m 23/665[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.2820 - loss: 1.7597  
[1m 51/665[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.2826 - loss: 1.7578
[1m 81/665[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.2945 - loss: 1.7321
[1m111/665[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.3050 - loss: 1.7090
[1m140/665[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3140 - loss: 1.6887
[1m171/665[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3230 - loss: 1.6698
[1m202/665[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3300 - loss: 1.6551
[1m230/665[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3353 - loss: 1.6432
[1m261/665[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3402 - loss: 1.6319
[1m292/665[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3445 - loss: 1.6224
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Epoch 2/26

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Epoch 3/26

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Epoch 4/26

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Epoch 5/26

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Epoch 6/26

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Epoch 7/26

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Epoch 8/26

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Epoch 9/26

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Epoch 10/26

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Epoch 11/26

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Epoch 12/26

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Epoch 13/26

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Epoch 14/26

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Epoch 15/26

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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m53s[0m 1s/step2025-11-04 15:16:34.465649: 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.

Accuracy capturado en la ejecución 16: 59.22 [%]
F1-score capturado en la ejecución 16: 58.74 [%]

=== EJECUCIÓN 17 ===

--- TRAIN (ejecución 17) ---

--- TEST (ejecución 17) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Loaded model from disk.
(1545, 6, 250)
(10634, 6, 250)

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7:24[0m 1s/step
[1m 55/333[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 926us/step
[1m115/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 880us/step
[1m172/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 881us/step
[1m232/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 869us/step
[1m292/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 864us/step
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step  
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[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 18ms/step
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[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 15ms/step
[1m46/96[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step 
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 11ms/step
Global accuracy score (validation) = 59.33 [%]
Global F1 score (validation) = 57.54 [%]
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[0.20211497 0.25529096 0.1863711  0.35622302]
 [0.39871484 0.40937147 0.15768638 0.03422735]
 [0.20319591 0.08960262 0.61692744 0.09027397]
 ...
 [0.05218079 0.00784223 0.91871685 0.02126006]
 [0.03759088 0.00494443 0.94243747 0.01502718]
 [0.13558127 0.02784291 0.7839351  0.05264075]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 77.51 [%]
Global accuracy score (test) = 59.61 [%]
Global F1 score (train) = 77.42 [%]
Global F1 score (test) = 58.38 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.45      0.30      0.36       400
MODERATE-INTENSITY       0.48      0.62      0.54       400
         SEDENTARY       0.72      0.92      0.80       400
VIGOROUS-INTENSITY       0.76      0.54      0.63       345

          accuracy                           0.60      1545
         macro avg       0.60      0.59      0.58      1545
      weighted avg       0.59      0.60      0.58      1545


Accuracy capturado en la ejecución 17: 59.61 [%]
2025-11-04 15:16:46.513290: 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`.
2025-11-04 15:16:46.524493: 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 00:00:1762265806.537495 1942324 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762265806.541589 1942324 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762265806.551279 1942324 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762265806.551296 1942324 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762265806.551297 1942324 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762265806.551298 1942324 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-04 15:16:46.554402: 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 00:00:1762265808.904409 1942324 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13768 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 1/26
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762265811.392852 1942437 service.cc:152] XLA service 0x75f54002b460 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762265811.392875 1942437 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-04 15:16:51.442121: 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 00:00:1762265811.750115 1942437 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762265813.759301 1942437 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 2/26

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Epoch 3/26

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Epoch 4/26

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Epoch 5/26

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Epoch 6/26

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Epoch 7/26

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Epoch 8/26

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Epoch 9/26

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Epoch 10/26

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Epoch 11/26

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Epoch 12/26

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Epoch 13/26

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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m52s[0m 1s/step2025-11-04 15:17:18.254606: 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.
F1-score capturado en la ejecución 17: 58.38 [%]

=== EJECUCIÓN 18 ===

--- TRAIN (ejecución 18) ---

--- TEST (ejecución 18) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Loaded model from disk.
(1545, 6, 250)
(10634, 6, 250)

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7:31[0m 1s/step
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Global accuracy score (validation) = 60.02 [%]
Global F1 score (validation) = 56.33 [%]
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[0.42153114 0.48361138 0.07923073 0.01562675]
 [0.17384772 0.11993559 0.05475877 0.65145785]
 [0.2971671  0.68774265 0.01106811 0.00402218]
 ...
 [0.11918671 0.02707145 0.7816374  0.0721045 ]
 [0.08919476 0.01742955 0.84318626 0.05018939]
 [0.07167377 0.01251025 0.87024695 0.04556903]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 72.09 [%]
Global accuracy score (test) = 60.91 [%]
Global F1 score (train) = 69.91 [%]
Global F1 score (test) = 57.73 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.50      0.18      0.26       400
MODERATE-INTENSITY       0.50      0.77      0.60       400
         SEDENTARY       0.70      0.91      0.79       400
VIGOROUS-INTENSITY       0.76      0.57      0.65       345

          accuracy                           0.61      1545
         macro avg       0.61      0.61      0.58      1545
      weighted avg       0.61      0.61      0.57      1545


Accuracy capturado en la ejecución 18: 60.91 [%]
2025-11-04 15:17:30.482631: 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`.
2025-11-04 15:17:30.493856: 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 00:00:1762265850.506824 1944754 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762265850.510779 1944754 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762265850.520556 1944754 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762265850.520574 1944754 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762265850.520575 1944754 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762265850.520577 1944754 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-04 15:17:30.523543: 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 00:00:1762265852.887927 1944754 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13768 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 1/26
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762265855.375860 1944885 service.cc:152] XLA service 0x7dd698017a80 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762265855.375886 1944885 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-04 15:17:35.424828: 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 00:00:1762265855.734043 1944885 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762265857.774665 1944885 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

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
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Epoch 2/26

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[1m 60/665[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.5008 - loss: 1.2079
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Epoch 3/26

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Epoch 4/26

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Epoch 5/26

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Epoch 6/26

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Epoch 7/26

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Epoch 8/26

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Epoch 9/26

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Epoch 10/26

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Epoch 11/26

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Epoch 12/26

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Epoch 13/26

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Saved model to disk.
F1-score capturado en la ejecución 18: 57.73 [%]

=== EJECUCIÓN 19 ===

--- TRAIN (ejecución 19) ---

--- TEST (ejecución 19) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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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Loaded model from disk.
(1545, 6, 250)
(10634, 6, 250)

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[1m57/96[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 894us/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step 
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 11ms/step
Global accuracy score (validation) = 59.59 [%]
Global F1 score (validation) = 57.69 [%]
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[0.3383485  0.65699655 0.00369392 0.00096096]
 [0.61775196 0.35491753 0.0235744  0.00375613]
 [0.4634029  0.4420774  0.07449695 0.02002284]
 ...
 [0.1262291  0.04279774 0.72996145 0.10101173]
 [0.11273992 0.03941661 0.7492245  0.09861899]
 [0.13880703 0.04790042 0.7151801  0.09811252]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 75.0 [%]
Global accuracy score (test) = 59.09 [%]
Global F1 score (train) = 74.78 [%]
Global F1 score (test) = 58.31 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.41      0.32      0.36       400
MODERATE-INTENSITY       0.47      0.59      0.52       400
         SEDENTARY       0.75      0.91      0.82       400
VIGOROUS-INTENSITY       0.75      0.54      0.63       345

          accuracy                           0.59      1545
         macro avg       0.60      0.59      0.58      1545
      weighted avg       0.59      0.59      0.58      1545

2025-11-04 15:18:14.445484: 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`.
2025-11-04 15:18:14.456794: 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 00:00:1762265894.470047 1947193 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762265894.474134 1947193 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762265894.483781 1947193 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762265894.483798 1947193 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762265894.483806 1947193 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762265894.483807 1947193 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-04 15:18:14.486946: 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 00:00:1762265896.834213 1947193 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13768 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 1/26
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762265899.340451 1947324 service.cc:152] XLA service 0x7f7e4802a2f0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762265899.340482 1947324 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-04 15:18:19.392760: 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 00:00:1762265899.716684 1947324 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762265901.719329 1947324 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 2/26

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Epoch 3/26

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Epoch 4/26

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Epoch 5/26

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Epoch 6/26

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Epoch 7/26

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Epoch 8/26

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Epoch 9/26

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Epoch 10/26

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Epoch 11/26

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Epoch 12/26

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Epoch 13/26

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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m52s[0m 1s/step2025-11-04 15:18:46.311230: 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.

Accuracy capturado en la ejecución 19: 59.09 [%]
F1-score capturado en la ejecución 19: 58.31 [%]

=== EJECUCIÓN 20 ===

--- TRAIN (ejecución 20) ---

--- TEST (ejecución 20) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Loaded model from disk.
(1545, 6, 250)
(10634, 6, 250)

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7:18[0m 1s/step
[1m 50/333[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step 
[1m102/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step
[1m158/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 974us/step
[1m216/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 947us/step
[1m273/333[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 934us/step
[1m327/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 935us/step
[1m333/333[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 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m52/96[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 982us/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step 
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 11ms/step
Global accuracy score (validation) = 57.23 [%]
Global F1 score (validation) = 55.64 [%]
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[0.41405347 0.25673187 0.21339259 0.11582214]
 [0.3576526  0.4371409  0.09503554 0.11017103]
 [0.26305735 0.32071632 0.23246163 0.1837647 ]
 ...
 [0.20565847 0.0915041  0.52586204 0.17697537]
 [0.03658057 0.00298468 0.94979614 0.01063861]
 [0.08410341 0.0115872  0.8671689  0.03714051]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 74.26 [%]
Global accuracy score (test) = 58.58 [%]
Global F1 score (train) = 74.16 [%]
Global F1 score (test) = 57.71 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.42      0.38      0.40       400
MODERATE-INTENSITY       0.50      0.51      0.51       400
         SEDENTARY       0.67      0.93      0.78       400
VIGOROUS-INTENSITY       0.81      0.51      0.63       345

          accuracy                           0.59      1545
         macro avg       0.60      0.58      0.58      1545
      weighted avg       0.59      0.59      0.58      1545

2025-11-04 15:18:58.468217: 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`.
2025-11-04 15:18:58.479416: 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 00:00:1762265938.492612 1949625 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762265938.496583 1949625 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762265938.506540 1949625 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762265938.506558 1949625 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762265938.506560 1949625 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762265938.506561 1949625 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-04 15:18:58.509705: 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 00:00:1762265940.886826 1949625 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13768 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 1/26
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762265943.412480 1949741 service.cc:152] XLA service 0x7e37d4016a90 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762265943.412524 1949741 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-04 15:19:03.465843: 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 00:00:1762265943.776477 1949741 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762265945.800252 1949741 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/665[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m46:48[0m 4s/step - accuracy: 0.1875 - loss: 2.1365
[1m 26/665[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.2358 - loss: 1.8776  
[1m 58/665[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.2777 - loss: 1.7916
[1m 86/665[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.2964 - loss: 1.7539
[1m117/665[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3118 - loss: 1.7253
[1m148/665[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3237 - loss: 1.7043
[1m179/665[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3330 - loss: 1.6875
[1m209/665[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3404 - loss: 1.6736
[1m240/665[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3467 - loss: 1.6602
[1m270/665[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3519 - loss: 1.6494
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[1m360/665[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3637 - loss: 1.6229
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Epoch 2/26

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Epoch 3/26

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Epoch 4/26

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Epoch 5/26

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[1m 30/665[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.5190 - loss: 1.0711  
[1m 61/665[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.5425 - loss: 1.0477
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Epoch 6/26

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Epoch 7/26

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Epoch 8/26

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Epoch 9/26

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Epoch 10/26

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Epoch 11/26

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Epoch 12/26

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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m53s[0m 1s/step2025-11-04 15:19:29.042039: 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.

Accuracy capturado en la ejecución 20: 58.58 [%]
F1-score capturado en la ejecución 20: 57.71 [%]

=== EJECUCIÓN 21 ===

--- TRAIN (ejecución 21) ---

--- TEST (ejecución 21) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
Loaded model from disk.
(1545, 6, 250)
(10634, 6, 250)

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[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m58/96[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 878us/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step 
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 11ms/step
Global accuracy score (validation) = 59.4 [%]
Global F1 score (validation) = 57.29 [%]
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[0.3090408  0.32431874 0.15421681 0.21242358]
 [0.08008819 0.79959106 0.027394   0.09292677]
 [0.38669467 0.44697338 0.11984321 0.04648875]
 ...
 [0.23541845 0.1157398  0.5869656  0.06187617]
 [0.10071761 0.03007365 0.831554   0.03765471]
 [0.09814754 0.0289416  0.83401    0.03890085]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 73.28 [%]
Global accuracy score (test) = 57.61 [%]
Global F1 score (train) = 72.41 [%]
Global F1 score (test) = 55.93 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.38      0.24      0.29       400
MODERATE-INTENSITY       0.47      0.63      0.54       400
         SEDENTARY       0.71      0.91      0.80       400
VIGOROUS-INTENSITY       0.75      0.52      0.61       345

          accuracy                           0.58      1545
         macro avg       0.58      0.57      0.56      1545
      weighted avg       0.57      0.58      0.56      1545


Accuracy capturado en la ejecución 21: 57.61 [%]
2025-11-04 15:19:41.100557: 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`.
2025-11-04 15:19:41.112436: 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 00:00:1762265981.126360 1951964 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762265981.130745 1951964 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762265981.141223 1951964 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762265981.141240 1951964 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762265981.141241 1951964 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762265981.141242 1951964 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-04 15:19:41.144393: 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 00:00:1762265983.483076 1951964 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13768 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 1/26
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762265985.965077 1952092 service.cc:152] XLA service 0x730e78014510 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762265985.965106 1952092 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-04 15:19:46.015147: 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 00:00:1762265986.325969 1952092 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762265988.352746 1952092 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/665[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m46:34[0m 4s/step - accuracy: 0.1875 - loss: 1.9178
[1m 25/665[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.2546 - loss: 1.9922  
[1m 55/665[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.2694 - loss: 1.9405
[1m 85/665[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.2849 - loss: 1.8870
[1m115/665[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2971 - loss: 1.8447
[1m146/665[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3063 - loss: 1.8138
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[1m202/665[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3195 - loss: 1.7715
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[1m296/665[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3365 - loss: 1.7171
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[1m356/665[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3449 - loss: 1.6905
[1m389/665[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3491 - loss: 1.6776
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Epoch 2/26

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Epoch 3/26

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Epoch 4/26

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Epoch 5/26

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Epoch 6/26

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Epoch 7/26

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Epoch 8/26

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Epoch 9/26

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Epoch 10/26

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Epoch 11/26

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Epoch 12/26

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Epoch 13/26

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Epoch 14/26

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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m54s[0m 1s/step2025-11-04 15:20:14.479242: 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.
F1-score capturado en la ejecución 21: 55.93 [%]

=== EJECUCIÓN 22 ===

--- TRAIN (ejecución 22) ---

--- TEST (ejecución 22) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Loaded model from disk.
(1545, 6, 250)
(10634, 6, 250)

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7:07[0m 1s/step
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[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m58/96[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 890us/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step 
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Global accuracy score (validation) = 59.46 [%]
Global F1 score (validation) = 58.28 [%]
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[0.62689847 0.25195315 0.10071707 0.02043127]
 [0.3660375  0.33113438 0.19927245 0.10355564]
 [0.19641475 0.78459173 0.01020037 0.00879319]
 ...
 [0.05582779 0.01144177 0.9104963  0.0222342 ]
 [0.06344491 0.01372837 0.90014756 0.02267929]
 [0.12380761 0.03963685 0.79735255 0.03920304]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 78.11 [%]
Global accuracy score (test) = 60.78 [%]
Global F1 score (train) = 78.09 [%]
Global F1 score (test) = 60.0 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.50      0.38      0.43       400
MODERATE-INTENSITY       0.49      0.58      0.53       400
         SEDENTARY       0.72      0.90      0.80       400
VIGOROUS-INTENSITY       0.73      0.56      0.64       345

          accuracy                           0.61      1545
         macro avg       0.61      0.61      0.60      1545
      weighted avg       0.61      0.61      0.60      1545


Accuracy capturado en la ejecución 22: 60.78 [%]
2025-11-04 15:20:26.594205: 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`.
2025-11-04 15:20:26.605237: 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 00:00:1762266026.618242 1954501 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762266026.622395 1954501 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762266026.632597 1954501 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762266026.632616 1954501 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762266026.632617 1954501 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762266026.632619 1954501 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-04 15:20:26.635626: 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 00:00:1762266028.978667 1954501 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13768 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 1/26
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762266031.526009 1954635 service.cc:152] XLA service 0x786f880261e0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762266031.526035 1954635 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-04 15:20:31.578644: 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 00:00:1762266031.888648 1954635 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762266033.910332 1954635 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 2/26

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Epoch 3/26

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Epoch 4/26

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Epoch 5/26

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Epoch 6/26

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Epoch 7/26

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Epoch 8/26

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Epoch 9/26

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Epoch 10/26

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Epoch 11/26

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Epoch 12/26

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[1m 32/665[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.7246 - loss: 0.7152  
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Epoch 13/26

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Epoch 14/26

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Epoch 15/26

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Epoch 16/26

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Saved model to disk.
F1-score capturado en la ejecución 22: 60.0 [%]

=== EJECUCIÓN 23 ===

--- TRAIN (ejecución 23) ---

--- TEST (ejecución 23) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Loaded model from disk.
(1545, 6, 250)
(10634, 6, 250)

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7:21[0m 1s/step
[1m 55/333[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 933us/step
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[1m55/96[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 930us/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step 
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Global accuracy score (validation) = 58.15 [%]
Global F1 score (validation) = 57.25 [%]
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[0.04587607 0.49339697 0.06429296 0.396434  ]
 [0.14660898 0.18844888 0.43378934 0.23115283]
 [0.41534987 0.19261725 0.3325955  0.05943739]
 ...
 [0.15436928 0.1169773  0.54933345 0.17931995]
 [0.11153013 0.02455099 0.8122086  0.05171034]
 [0.10005748 0.02096708 0.835699   0.04327643]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 79.74 [%]
Global accuracy score (test) = 58.25 [%]
Global F1 score (train) = 79.84 [%]
Global F1 score (test) = 57.43 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.41      0.36      0.38       400
MODERATE-INTENSITY       0.49      0.50      0.50       400
         SEDENTARY       0.70      0.92      0.79       400
VIGOROUS-INTENSITY       0.74      0.54      0.62       345

          accuracy                           0.58      1545
         macro avg       0.58      0.58      0.57      1545
      weighted avg       0.58      0.58      0.57      1545


Accuracy capturado en la ejecución 23: 58.25 [%]
2025-11-04 15:21:14.766701: 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`.
2025-11-04 15:21:14.777765: 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 00:00:1762266074.790828 1957256 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762266074.794886 1957256 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762266074.804628 1957256 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762266074.804644 1957256 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762266074.804646 1957256 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762266074.804647 1957256 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-04 15:21:14.807785: 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 00:00:1762266077.143068 1957256 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13768 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 1/26
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762266079.637753 1957366 service.cc:152] XLA service 0x767fe4015820 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762266079.637805 1957366 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-04 15:21:19.691776: 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 00:00:1762266080.004249 1957366 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762266082.050965 1957366 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 2/26

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Epoch 3/26

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Epoch 4/26

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Epoch 5/26

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Epoch 6/26

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Epoch 7/26

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Epoch 8/26

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Epoch 9/26

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Epoch 10/26

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Epoch 11/26

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Epoch 12/26

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Epoch 13/26

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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m50s[0m 1s/step2025-11-04 15:21:46.514705: 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.
F1-score capturado en la ejecución 23: 57.43 [%]

=== EJECUCIÓN 24 ===

--- TRAIN (ejecución 24) ---

--- TEST (ejecución 24) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Loaded model from disk.
(1545, 6, 250)
(10634, 6, 250)

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7:02[0m 1s/step
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[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m56/96[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 924us/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step 
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 11ms/step
Global accuracy score (validation) = 58.71 [%]
Global F1 score (validation) = 56.76 [%]
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[0.5460395  0.40852883 0.03754104 0.0078906 ]
 [0.27250004 0.1837887  0.3728703  0.17084096]
 [0.21147732 0.68505543 0.04532788 0.05813931]
 ...
 [0.11003742 0.01847173 0.81795496 0.05353592]
 [0.11003742 0.01847173 0.81795496 0.05353592]
 [0.08252749 0.01154388 0.86392355 0.04200507]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 71.15 [%]
Global accuracy score (test) = 57.54 [%]
Global F1 score (train) = 70.51 [%]
Global F1 score (test) = 55.96 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.38      0.27      0.31       400
MODERATE-INTENSITY       0.47      0.51      0.49       400
         SEDENTARY       0.69      0.94      0.80       400
VIGOROUS-INTENSITY       0.71      0.58      0.64       345

          accuracy                           0.58      1545
         macro avg       0.56      0.58      0.56      1545
      weighted avg       0.56      0.58      0.56      1545

2025-11-04 15:21:58.466644: 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`.
2025-11-04 15:21:58.477923: 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 00:00:1762266118.490998 1959665 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762266118.495075 1959665 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762266118.504920 1959665 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762266118.504939 1959665 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762266118.504940 1959665 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762266118.504941 1959665 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-04 15:21:58.508056: 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 00:00:1762266120.886291 1959665 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13768 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 1/26
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762266123.397449 1959795 service.cc:152] XLA service 0x731f70005f40 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762266123.397510 1959795 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-04 15:22:03.447728: 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 00:00:1762266123.769794 1959795 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762266125.784292 1959795 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 2/26

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Epoch 3/26

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Epoch 4/26

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Epoch 5/26

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Epoch 6/26

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Epoch 7/26

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Epoch 8/26

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Epoch 9/26

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Epoch 10/26

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Epoch 11/26

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Epoch 12/26

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Epoch 13/26

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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m53s[0m 1s/step2025-11-04 15:22:30.203154: 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.

Accuracy capturado en la ejecución 24: 57.54 [%]
F1-score capturado en la ejecución 24: 55.96 [%]

=== EJECUCIÓN 25 ===

--- TRAIN (ejecución 25) ---

--- TEST (ejecución 25) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (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)
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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)
Loaded model from disk.
(1545, 6, 250)
(10634, 6, 250)

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[1m56/96[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 913us/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step 
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 11ms/step
Global accuracy score (validation) = 59.4 [%]
Global F1 score (validation) = 57.7 [%]
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[0.43891305 0.47692356 0.0640794  0.02008404]
 [0.09571903 0.8954109  0.00312732 0.00574267]
 [0.63377583 0.29935986 0.02876215 0.03810214]
 ...
 [0.06424796 0.01178454 0.8876622  0.0363053 ]
 [0.18411969 0.08166346 0.63596267 0.09825423]
 [0.04437303 0.00653015 0.92205983 0.02703698]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 76.37 [%]
Global accuracy score (test) = 58.96 [%]
Global F1 score (train) = 76.24 [%]
Global F1 score (test) = 58.33 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.41      0.33      0.36       400
MODERATE-INTENSITY       0.47      0.60      0.53       400
         SEDENTARY       0.73      0.90      0.81       400
VIGOROUS-INTENSITY       0.81      0.52      0.64       345

          accuracy                           0.59      1545
         macro avg       0.61      0.59      0.58      1545
      weighted avg       0.60      0.59      0.58      1545


Accuracy capturado en la ejecución 25: 58.96 [%]
2025-11-04 15:22:42.341536: 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`.
2025-11-04 15:22:42.352846: 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 00:00:1762266162.365896 1962102 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762266162.370026 1962102 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762266162.379801 1962102 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762266162.379818 1962102 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762266162.379820 1962102 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762266162.379821 1962102 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-04 15:22:42.383019: 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 00:00:1762266164.749428 1962102 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13766 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 1/26
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762266167.259750 1962235 service.cc:152] XLA service 0x73ceb4017e60 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762266167.259776 1962235 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-04 15:22:47.311394: 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 00:00:1762266167.620752 1962235 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762266169.635728 1962235 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 52/665[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.2433 - loss: 1.9328
[1m 83/665[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.2676 - loss: 1.8642
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Epoch 2/26

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Epoch 3/26

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Epoch 4/26

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Epoch 5/26

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Epoch 6/26

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Epoch 7/26

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Epoch 8/26

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Epoch 9/26

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Epoch 10/26

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Epoch 11/26

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Epoch 12/26

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Epoch 13/26

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Epoch 14/26

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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m51s[0m 1s/step2025-11-04 15:23:15.571928: 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.
F1-score capturado en la ejecución 25: 58.33 [%]

=== EJECUCIÓN 26 ===

--- TRAIN (ejecución 26) ---

--- TEST (ejecución 26) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Loaded model from disk.
(1545, 6, 250)
(10634, 6, 250)

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7:02[0m 1s/step
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[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m54/96[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 950us/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step 
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 11ms/step
Global accuracy score (validation) = 57.82 [%]
Global F1 score (validation) = 56.63 [%]
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[0.6533396  0.28439948 0.04278468 0.01947632]
 [0.63914716 0.27850944 0.06482731 0.01751619]
 [0.13334185 0.61761487 0.03988211 0.2091612 ]
 ...
 [0.08857951 0.02069131 0.8434921  0.04723703]
 [0.03391588 0.00538136 0.93847156 0.02223122]
 [0.05137118 0.00946805 0.90795606 0.03120477]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 73.65 [%]
Global accuracy score (test) = 57.86 [%]
Global F1 score (train) = 73.52 [%]
Global F1 score (test) = 57.49 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.40      0.45      0.42       400
MODERATE-INTENSITY       0.49      0.40      0.44       400
         SEDENTARY       0.68      0.91      0.78       400
VIGOROUS-INTENSITY       0.81      0.56      0.66       345

          accuracy                           0.58      1545
         macro avg       0.60      0.58      0.57      1545
      weighted avg       0.59      0.58      0.57      1545


Accuracy capturado en la ejecución 26: 57.86 [%]
2025-11-04 15:23:27.604899: 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`.
2025-11-04 15:23:27.616619: 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 00:00:1762266207.630802 1964635 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762266207.635128 1964635 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762266207.645393 1964635 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762266207.645413 1964635 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762266207.645414 1964635 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762266207.645416 1964635 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-04 15:23:27.648442: 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 00:00:1762266210.000845 1964635 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13768 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 1/26
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762266212.500142 1964765 service.cc:152] XLA service 0x75fddc014c90 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762266212.500187 1964765 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-04 15:23:32.564094: 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 00:00:1762266212.890254 1964765 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762266214.942891 1964765 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 2/26

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Epoch 3/26

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Epoch 4/26

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Epoch 5/26

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Epoch 6/26

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Epoch 7/26

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Epoch 8/26

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Epoch 9/26

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Epoch 10/26

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Epoch 11/26

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Epoch 12/26

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Saved model to disk.
F1-score capturado en la ejecución 26: 57.49 [%]

=== EJECUCIÓN 27 ===

--- TRAIN (ejecución 27) ---

--- TEST (ejecución 27) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Loaded model from disk.
(1545, 6, 250)
(10634, 6, 250)

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7:20[0m 1s/step
[1m 55/333[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 936us/step
[1m114/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 890us/step
[1m162/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 936us/step
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[1m272/333[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 931us/step
[1m330/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 920us/step
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step  
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[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m54/96[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 947us/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step 
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 11ms/step
Global accuracy score (validation) = 60.84 [%]
Global F1 score (validation) = 60.32 [%]
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[0.3590273  0.30957133 0.22245948 0.10894193]
 [0.12671459 0.7497172  0.0266856  0.09688263]
 [0.47927728 0.4238878  0.06920295 0.02763208]
 ...
 [0.20450892 0.06743715 0.6620262  0.06602775]
 [0.15915202 0.04252807 0.74622077 0.05209917]
 [0.1569902  0.04104493 0.7514074  0.05055754]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 74.16 [%]
Global accuracy score (test) = 58.64 [%]
Global F1 score (train) = 74.65 [%]
Global F1 score (test) = 58.72 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.42      0.40      0.41       400
MODERATE-INTENSITY       0.48      0.59      0.53       400
         SEDENTARY       0.76      0.82      0.79       400
VIGOROUS-INTENSITY       0.75      0.53      0.62       345

          accuracy                           0.59      1545
         macro avg       0.60      0.58      0.59      1545
      weighted avg       0.60      0.59      0.59      1545

2025-11-04 15:24:10.199391: 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`.
2025-11-04 15:24:10.211039: 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 00:00:1762266250.224712 1966951 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762266250.228853 1966951 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762266250.239262 1966951 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762266250.239280 1966951 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762266250.239281 1966951 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762266250.239283 1966951 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-04 15:24:10.242235: 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 00:00:1762266252.583203 1966951 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13768 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 1/26
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762266255.091375 1967079 service.cc:152] XLA service 0x772d700025e0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762266255.091403 1967079 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-04 15:24:15.141504: 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 00:00:1762266255.450311 1967079 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762266257.474661 1967079 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 2/26

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Epoch 3/26

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Epoch 4/26

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Epoch 5/26

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Epoch 6/26

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Epoch 7/26

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Epoch 8/26

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Epoch 9/26

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Epoch 10/26

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Epoch 11/26

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Epoch 12/26

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Epoch 13/26

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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m52s[0m 1s/step2025-11-04 15:24:41.968241: 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.

Accuracy capturado en la ejecución 27: 58.64 [%]
F1-score capturado en la ejecución 27: 58.72 [%]

=== EJECUCIÓN 28 ===

--- TRAIN (ejecución 28) ---

--- TEST (ejecución 28) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
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Loaded model from disk.
(1545, 6, 250)
(10634, 6, 250)

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7:07[0m 1s/step
[1m 50/333[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step 
[1m111/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 926us/step
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[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step  
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[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 18ms/step
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[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m50/96[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step 
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 11ms/step
Global accuracy score (validation) = 60.55 [%]
Global F1 score (validation) = 59.47 [%]
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[0.44948277 0.40356323 0.08838426 0.05856975]
 [0.6146451  0.3277067  0.04399943 0.01364882]
 [0.2605574  0.14508596 0.4591029  0.13525367]
 ...
 [0.0468782  0.01618968 0.91174567 0.02518641]
 [0.03277669 0.01069292 0.9390934  0.01743697]
 [0.08839773 0.03393407 0.83903086 0.0386373 ]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 75.78 [%]
Global accuracy score (test) = 60.13 [%]
Global F1 score (train) = 75.81 [%]
Global F1 score (test) = 59.51 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.43      0.38      0.40       400
MODERATE-INTENSITY       0.50      0.54      0.52       400
         SEDENTARY       0.71      0.93      0.81       400
VIGOROUS-INTENSITY       0.81      0.55      0.66       345

          accuracy                           0.60      1545
         macro avg       0.61      0.60      0.60      1545
      weighted avg       0.60      0.60      0.59      1545

2025-11-04 15:24:54.016274: 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`.
2025-11-04 15:24:54.027731: 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 00:00:1762266294.041434 1969409 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762266294.045694 1969409 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762266294.056493 1969409 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762266294.056516 1969409 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762266294.056517 1969409 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762266294.056518 1969409 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-04 15:24:54.059892: 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 00:00:1762266296.411626 1969409 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13768 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 1/26
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762266298.894649 1969525 service.cc:152] XLA service 0x770f68004570 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762266298.894694 1969525 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-04 15:24:58.946471: 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 00:00:1762266299.281226 1969525 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762266301.306178 1969525 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 2/26

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Epoch 3/26

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Epoch 4/26

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Epoch 5/26

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Epoch 6/26

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Epoch 7/26

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Epoch 8/26

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Epoch 9/26

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Epoch 10/26

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Epoch 11/26

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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m55s[0m 1s/step
[1m48/49[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step2025-11-04 15:25:22.861669: 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.

Accuracy capturado en la ejecución 28: 60.13 [%]
F1-score capturado en la ejecución 28: 59.51 [%]

=== EJECUCIÓN 29 ===

--- TRAIN (ejecución 29) ---

--- TEST (ejecución 29) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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)
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This activity can't be balanced (in a downsampling way)
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Loaded model from disk.
(1545, 6, 250)
(10634, 6, 250)

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[1m288/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 880us/step
[1m333/333[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 17ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 18ms/step

[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m55/96[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 936us/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step 
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 11ms/step
Global accuracy score (validation) = 57.23 [%]
Global F1 score (validation) = 55.81 [%]
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[0.32434502 0.19079918 0.31937405 0.16548173]
 [0.45243114 0.43331596 0.07296459 0.04128832]
 [0.5276751  0.24989931 0.16660894 0.0558166 ]
 ...
 [0.07663916 0.01658894 0.8542422  0.0525297 ]
 [0.09643035 0.02539184 0.8002502  0.07792771]
 [0.05866776 0.01106705 0.89091974 0.03934554]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 72.3 [%]
Global accuracy score (test) = 58.45 [%]
Global F1 score (train) = 72.23 [%]
Global F1 score (test) = 57.98 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.41      0.46      0.44       400
MODERATE-INTENSITY       0.49      0.42      0.46       400
         SEDENTARY       0.71      0.91      0.80       400
VIGOROUS-INTENSITY       0.77      0.53      0.63       345

          accuracy                           0.58      1545
         macro avg       0.60      0.58      0.58      1545
      weighted avg       0.59      0.58      0.58      1545


Accuracy capturado en la ejecución 29: 58.45 [%]
2025-11-04 15:25:34.980433: 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`.
2025-11-04 15:25:34.991885: 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 00:00:1762266335.006059 1971624 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762266335.010424 1971624 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762266335.020737 1971624 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762266335.020753 1971624 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762266335.020755 1971624 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762266335.020756 1971624 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-04 15:25:35.023993: 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 00:00:1762266337.371419 1971624 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13768 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 1/26
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762266339.928910 1971762 service.cc:152] XLA service 0x74be140144a0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762266339.928935 1971762 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-04 15:25:39.979548: 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 00:00:1762266340.302328 1971762 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762266342.373809 1971762 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/665[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m47:37[0m 4s/step - accuracy: 0.2500 - loss: 2.1089
[1m 23/665[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.1972 - loss: 2.0153  
[1m 54/665[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.2432 - loss: 1.8834
[1m 84/665[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.2720 - loss: 1.8113
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[1m170/665[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3148 - loss: 1.7002
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Epoch 2/26

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Epoch 3/26

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Epoch 4/26

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Epoch 5/26

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Epoch 6/26

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Epoch 7/26

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Epoch 8/26

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Epoch 9/26

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Epoch 10/26

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Epoch 11/26

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Epoch 12/26

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Epoch 13/26

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Epoch 14/26

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Epoch 15/26

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Saved model to disk.
F1-score capturado en la ejecución 29: 57.98 [%]

=== EJECUCIÓN 30 ===

--- TRAIN (ejecución 30) ---

--- TEST (ejecución 30) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (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, 6, 250)
(10634, 6, 250)

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6:50[0m 1s/step
[1m 46/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step 
[1m 99/333[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step
[1m159/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 963us/step
[1m207/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 984us/step
[1m264/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 963us/step
[1m324/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 939us/step
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step  
[1m333/333[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 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 15ms/step
[1m53/96[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 972us/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step 
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 10ms/step
Global accuracy score (validation) = 59.26 [%]
Global F1 score (validation) = 58.1 [%]
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[0.11099499 0.20343873 0.07759713 0.60796916]
 [0.7055629  0.26805902 0.02360481 0.00277334]
 [0.5311606  0.29418388 0.11281706 0.06183848]
 ...
 [0.07538618 0.02285325 0.8479699  0.05379069]
 [0.12877819 0.04432701 0.75515765 0.0717372 ]
 [0.07866518 0.02390991 0.83919454 0.05823037]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 78.2 [%]
Global accuracy score (test) = 61.1 [%]
Global F1 score (train) = 78.18 [%]
Global F1 score (test) = 60.21 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.49      0.38      0.43       400
MODERATE-INTENSITY       0.50      0.63      0.56       400
         SEDENTARY       0.75      0.90      0.82       400
VIGOROUS-INTENSITY       0.72      0.52      0.60       345

          accuracy                           0.61      1545
         macro avg       0.61      0.61      0.60      1545
      weighted avg       0.61      0.61      0.60      1545


Accuracy capturado en la ejecución 30: 61.1 [%]
F1-score capturado en la ejecución 30: 60.21 [%]

=== RESUMEN FINAL ===
Accuracies: [60.52, 59.87, 59.09, 57.67, 59.03, 57.09, 59.42, 58.38, 58.83, 60.13, 60.97, 59.81, 59.35, 57.22, 58.38, 59.22, 59.61, 60.91, 59.09, 58.58, 57.61, 60.78, 58.25, 57.54, 58.96, 57.86, 58.64, 60.13, 58.45, 61.1]
F1-scores: [59.36, 59.25, 57.0, 54.99, 59.61, 56.54, 58.84, 57.18, 57.91, 58.96, 60.75, 58.27, 58.32, 57.96, 57.64, 58.74, 58.38, 57.73, 58.31, 57.71, 55.93, 60.0, 57.43, 55.96, 58.33, 57.49, 58.72, 59.51, 57.98, 60.21]
Accuracy mean: 59.0830 | std: 1.1230
F1 mean: 58.1670 | std: 1.2820

Resultados guardados en /mnt/nvme1n2/git/uniovi-simur-wearablepermed-data/output/Paper_results/cases_dataset_C/case_C_CAPTURE24_acc_gyr_superclasses_CPA_METs/metrics_test.npz
