2025-11-08 16:39:53.769421: 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`.
2025-11-08 16:39:53.781279: 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 00:00:1762616393.795479 1159025 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762616393.799976 1159025 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762616393.810595 1159025 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762616393.810639 1159025 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762616393.810641 1159025 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762616393.810643 1159025 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-08 16:39:53.814036: 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(
2025-11-08 16:39:56,937	INFO worker.py:1927 -- Started a local Ray instance.
2025-11-08 16:39:57,641	INFO tune.py:253 -- Initializing Ray automatically. For cluster usage or custom Ray initialization, call `ray.init(...)` before `Tuner(...)`.
2025-11-08 16:39:57,708	INFO trial.py:182 -- Creating a new dirname dir_2e133_2f62 because trial dirname 'dir_2e133' already exists.
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2025-11-08 16:39:57,716	INFO trial.py:182 -- Creating a new dirname dir_2e133_5343 because trial dirname 'dir_2e133' already exists.
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2025-11-08 16:39:57,732	INFO trial.py:182 -- Creating a new dirname dir_2e133_3034 because trial dirname 'dir_2e133' already exists.
2025-11-08 16:39:57,735	INFO trial.py:182 -- Creating a new dirname dir_2e133_d54a because trial dirname 'dir_2e133' already exists.
2025-11-08 16:39:57,738	INFO trial.py:182 -- Creating a new dirname dir_2e133_d537 because trial dirname 'dir_2e133' already exists.
2025-11-08 16:39:57,741	INFO trial.py:182 -- Creating a new dirname dir_2e133_d6a0 because trial dirname 'dir_2e133' already exists.
2025-11-08 16:39:57,744	INFO trial.py:182 -- Creating a new dirname dir_2e133_ebd3 because trial dirname 'dir_2e133' already exists.
2025-11-08 16:39:57,747	INFO trial.py:182 -- Creating a new dirname dir_2e133_ed47 because trial dirname 'dir_2e133' already exists.
2025-11-08 16:39:57,751	INFO trial.py:182 -- Creating a new dirname dir_2e133_d928 because trial dirname 'dir_2e133' already exists.
2025-11-08 16:39:57,759	INFO trial.py:182 -- Creating a new dirname dir_2e133_3dc7 because trial dirname 'dir_2e133' already exists.
2025-11-08 16:39:57,767	INFO trial.py:182 -- Creating a new dirname dir_2e133_bff9 because trial dirname 'dir_2e133' already exists.
2025-11-08 16:39:57,773	INFO trial.py:182 -- Creating a new dirname dir_2e133_873d because trial dirname 'dir_2e133' 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_PI/case_PI_CAPTURE24_acc_gyr_superclasses_CPA_METs/CAPTURE24_hyperparameters_tuning
To visualize your results with TensorBoard, run: `tensorboard --logdir /tmp/ray/session_2025-11-08_16-39-56_216796_1159025/artifacts/2025-11-08_16-39-57/CAPTURE24_hyperparameters_tuning/driver_artifacts`

Trial status: 20 PENDING
Current time: 2025-11-08 16:39:57. Total running time: 0s
Logical resource usage: 0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status       N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     num_resblocks     tasa_aprendizaje     epochs │
├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_2e133    PENDING            2   rmsprop         relu                                   16                 96                  3                 1          6.07697e-05         25 │
│ trial_2e133    PENDING            3   adam            relu                                   32                 32                  5                 1          1.30564e-05         27 │
│ trial_2e133    PENDING            3   adam            relu                                   32                 96                  3                 0          3.11011e-05         20 │
│ trial_2e133    PENDING            2   adam            tanh                                   16                 96                  3                 1          0.00329571          17 │
│ trial_2e133    PENDING            2   adam            tanh                                   32                 96                  3                 0          0.000165898         20 │
│ trial_2e133    PENDING            3   rmsprop         relu                                   32                 64                  5                 1          0.00302727          18 │
│ trial_2e133    PENDING            2   adam            relu                                   16                 32                  5                 0          8.69755e-05         18 │
│ trial_2e133    PENDING            3   adam            relu                                   16                 32                  5                 0          0.000149645         28 │
│ trial_2e133    PENDING            2   rmsprop         relu                                   16                 32                  5                 1          0.00234329          17 │
│ trial_2e133    PENDING            2   rmsprop         tanh                                   16                 96                  5                 1          0.00031027          18 │
│ trial_2e133    PENDING            2   adam            tanh                                   16                 64                  3                 0          0.000308353         26 │
│ trial_2e133    PENDING            3   rmsprop         relu                                   32                 32                  5                 0          0.00111465          17 │
│ trial_2e133    PENDING            2   rmsprop         tanh                                   16                 32                  5                 0          0.000595899         20 │
│ trial_2e133    PENDING            2   adam            tanh                                   32                 32                  3                 0          0.000147791         24 │
│ trial_2e133    PENDING            3   rmsprop         tanh                                   32                 64                  5                 0          2.69047e-05         23 │
│ trial_2e133    PENDING            2   adam            relu                                   16                 32                  5                 0          0.000225987         26 │
│ trial_2e133    PENDING            3   rmsprop         tanh                                   16                 64                  5                 0          4.29062e-05         17 │
│ trial_2e133    PENDING            3   rmsprop         tanh                                   32                 64                  5                 0          0.00161065          19 │
│ trial_2e133    PENDING            3   rmsprop         relu                                   16                 64                  3                 1          0.000108525         28 │
│ trial_2e133    PENDING            2   rmsprop         relu                                   32                 96                  3                 1          1.93582e-05         23 │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

Trial trial_2e133 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_2e133 config             │
├──────────────────────────────────────┤
│ N_capas                            3 │
│ epochs                            20 │
│ funcion_activacion              relu │
│ num_resblocks                      0 │
│ numero_filtros                    96 │
│ optimizador                     adam │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00003 │
╰──────────────────────────────────────╯
Trial trial_2e133 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_2e133 config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                            25 │
│ funcion_activacion              relu │
│ num_resblocks                      1 │
│ numero_filtros                    96 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  16 │
│ tasa_aprendizaje             0.00006 │
╰──────────────────────────────────────╯
Trial trial_2e133 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_2e133 config             │
├──────────────────────────────────────┤
│ N_capas                            3 │
│ epochs                            19 │
│ funcion_activacion              tanh │
│ num_resblocks                      0 │
│ numero_filtros                    64 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00161 │
╰──────────────────────────────────────╯
Trial trial_2e133 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_2e133 config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                            26 │
│ funcion_activacion              tanh │
│ num_resblocks                      0 │
│ numero_filtros                    64 │
│ optimizador                     adam │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  16 │
│ tasa_aprendizaje             0.00031 │
╰──────────────────────────────────────╯
Trial trial_2e133 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_2e133 config             │
├──────────────────────────────────────┤
│ N_capas                            3 │
│ epochs                            28 │
│ funcion_activacion              relu │
│ num_resblocks                      0 │
│ numero_filtros                    32 │
│ optimizador                     adam │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  16 │
│ tasa_aprendizaje             0.00015 │
╰──────────────────────────────────────╯
Trial trial_2e133 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_2e133 config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                            24 │
│ funcion_activacion              tanh │
│ num_resblocks                      0 │
│ numero_filtros                    32 │
│ optimizador                     adam │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00015 │
╰──────────────────────────────────────╯
Trial trial_2e133 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_2e133 config             │
├──────────────────────────────────────┤
│ N_capas                            3 │
│ epochs                            27 │
│ funcion_activacion              relu │
│ num_resblocks                      1 │
│ numero_filtros                    32 │
│ optimizador                     adam │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00001 │
╰──────────────────────────────────────╯
Trial trial_2e133 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_2e133 config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                            20 │
│ funcion_activacion              tanh │
│ num_resblocks                      0 │
│ numero_filtros                    96 │
│ optimizador                     adam │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00017 │
╰──────────────────────────────────────╯
Trial trial_2e133 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_2e133 config             │
├──────────────────────────────────────┤
│ N_capas                            3 │
│ epochs                            17 │
│ funcion_activacion              relu │
│ num_resblocks                      0 │
│ numero_filtros                    32 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00111 │
╰──────────────────────────────────────╯
Trial trial_2e133 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_2e133 config             │
├──────────────────────────────────────┤
│ N_capas                            3 │
│ epochs                            23 │
│ funcion_activacion              tanh │
│ num_resblocks                      0 │
│ numero_filtros                    64 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00003 │
╰──────────────────────────────────────╯
Trial trial_2e133 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_2e133 config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                            23 │
│ funcion_activacion              relu │
│ num_resblocks                      1 │
│ numero_filtros                    96 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00002 │
╰──────────────────────────────────────╯
Trial trial_2e133 started with configuration:
[36m(train_cnn_ray_tune pid=1160657)[0m 2025-11-08 16:40:00.920158: 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=1160657)[0m 2025-11-08 16:40:00.942022: 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=1160661)[0m WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
[36m(train_cnn_ray_tune pid=1160661)[0m E0000 00:00:1762616400.945954 1161818 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=1160661)[0m E0000 00:00:1762616400.954373 1161818 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=1160661)[0m W0000 00:00:1762616400.974894 1161818 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=1160661)[0m W0000 00:00:1762616400.974939 1161818 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=1160661)[0m W0000 00:00:1762616400.974942 1161818 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=1160661)[0m W0000 00:00:1762616400.974944 1161818 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=1160657)[0m 2025-11-08 16:40:01.004153: 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=1160657)[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=1160657)[0m 2025-11-08 16:40:04.201598: 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=1160657)[0m 2025-11-08 16:40:04.201658: I external/local_xla/xla/stream_executor/cuda/cuda_diagnostics.cc:167] env: CUDA_VISIBLE_DEVICES=""
[36m(train_cnn_ray_tune pid=1160657)[0m 2025-11-08 16:40:04.201667: 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=1160657)[0m 2025-11-08 16:40:04.201672: 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=1160657)[0m 2025-11-08 16:40:04.201678: 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=1160657)[0m 2025-11-08 16:40:04.201681: I external/local_xla/xla/stream_executor/cuda/cuda_diagnostics.cc:190] hostname: simur-MS-7B94
[36m(train_cnn_ray_tune pid=1160657)[0m 2025-11-08 16:40:04.201903: 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=1160657)[0m 2025-11-08 16:40:04.201939: 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=1160657)[0m 2025-11-08 16:40:04.201944: I external/local_xla/xla/stream_executor/cuda/cuda_diagnostics.cc:291] kernel version seems to match DSO: 570.133.7
╭──────────────────────────────────────╮
│ Trial trial_2e133 config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                            18 │
│ funcion_activacion              relu │
│ num_resblocks                      0 │
│ numero_filtros                    32 │
│ optimizador                     adam │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  16 │
│ tasa_aprendizaje             0.00009 │
╰──────────────────────────────────────╯
Trial trial_2e133 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_2e133 config             │
├──────────────────────────────────────┤
│ N_capas                            3 │
│ epochs                            28 │
│ funcion_activacion              relu │
│ num_resblocks                      1 │
│ numero_filtros                    64 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  16 │
│ tasa_aprendizaje             0.00011 │
╰──────────────────────────────────────╯
Trial trial_2e133 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_2e133 config             │
├──────────────────────────────────────┤
│ N_capas                            3 │
│ epochs                            17 │
│ funcion_activacion              tanh │
│ num_resblocks                      0 │
│ numero_filtros                    64 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  16 │
│ tasa_aprendizaje             0.00004 │
╰──────────────────────────────────────╯
Trial trial_2e133 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_2e133 config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                            18 │
│ funcion_activacion              tanh │
│ num_resblocks                      1 │
│ numero_filtros                    96 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  16 │
│ tasa_aprendizaje             0.00031 │
╰──────────────────────────────────────╯
Trial trial_2e133 started with configuration:
╭─────────────────────────────────────╮
│ Trial trial_2e133 config            │
├─────────────────────────────────────┤
│ N_capas                           2 │
│ epochs                           17 │
│ funcion_activacion             tanh │
│ num_resblocks                     1 │
│ numero_filtros                   96 │
│ optimizador                    adam │
│ tamanho_filtro                    3 │
│ tamanho_minilote                 16 │
│ tasa_aprendizaje             0.0033 │
╰─────────────────────────────────────╯
Trial trial_2e133 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_2e133 config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                            20 │
│ funcion_activacion              tanh │
│ num_resblocks                      0 │
│ numero_filtros                    32 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  16 │
│ tasa_aprendizaje              0.0006 │
╰──────────────────────────────────────╯
Trial trial_2e133 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_2e133 config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                            26 │
│ funcion_activacion              relu │
│ num_resblocks                      0 │
│ numero_filtros                    32 │
│ optimizador                     adam │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  16 │
│ tasa_aprendizaje             0.00023 │
╰──────────────────────────────────────╯
Trial trial_2e133 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_2e133 config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                            17 │
│ funcion_activacion              relu │
│ num_resblocks                      1 │
│ numero_filtros                    32 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  16 │
│ tasa_aprendizaje             0.00234 │
╰──────────────────────────────────────╯
Trial trial_2e133 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_2e133 config             │
├──────────────────────────────────────┤
│ N_capas                            3 │
│ epochs                            18 │
│ funcion_activacion              relu │
│ num_resblocks                      1 │
│ numero_filtros                    64 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00303 │
╰──────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=1160657)[0m Epoch 1/25
[36m(train_cnn_ray_tune pid=1160657)[0m 
[1m  1/619[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m30:25[0m 3s/step - accuracy: 0.3125 - loss: 1.6840
[36m(train_cnn_ray_tune pid=1160657)[0m 
[1m  4/619[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 22ms/step - accuracy: 0.3633 - loss: 1.8743
[1m  7/619[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 22ms/step - accuracy: 0.3759 - loss: 1.8209
[36m(train_cnn_ray_tune pid=1160657)[0m 
[1m 10/619[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m14s[0m 24ms/step - accuracy: 0.3820 - loss: 1.7774
[36m(train_cnn_ray_tune pid=1160657)[0m 
[1m 12/619[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m14s[0m 25ms/step - accuracy: 0.3809 - loss: 1.7622
[1m 14/619[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m15s[0m 26ms/step - accuracy: 0.3794 - loss: 1.7537
[36m(train_cnn_ray_tune pid=1160657)[0m 
[1m 16/619[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m15s[0m 26ms/step - accuracy: 0.3788 - loss: 1.7451
[1m 19/619[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m15s[0m 25ms/step - accuracy: 0.3793 - loss: 1.7330
[36m(train_cnn_ray_tune pid=1160657)[0m 
[1m 22/619[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m14s[0m 25ms/step - accuracy: 0.3792 - loss: 1.7244
[36m(train_cnn_ray_tune pid=1160657)[0m 
[1m 25/619[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m14s[0m 24ms/step - accuracy: 0.3784 - loss: 1.7216
[1m 27/619[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m14s[0m 25ms/step - accuracy: 0.3787 - loss: 1.7196
[36m(train_cnn_ray_tune pid=1160657)[0m 
[1m 30/619[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m14s[0m 25ms/step - accuracy: 0.3800 - loss: 1.7167
[36m(train_cnn_ray_tune pid=1160658)[0m 
[1m  1/619[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m28:40[0m 3s/step - accuracy: 0.0625 - loss: 2.5114
[1m  3/619[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m16s[0m 26ms/step - accuracy: 0.0938 - loss: 2.3110
[36m(train_cnn_ray_tune pid=1160657)[0m 
[1m 33/619[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m14s[0m 25ms/step - accuracy: 0.3822 - loss: 1.7124
[36m(train_cnn_ray_tune pid=1160670)[0m Epoch 1/18[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=1160681)[0m 
[1m  3/310[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m32s[0m 107ms/step - accuracy: 0.3698 - loss: 1.4723
[1m  4/310[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m30s[0m 98ms/step - accuracy: 0.3789 - loss: 1.4833 
[36m(train_cnn_ray_tune pid=1160681)[0m 
[1m  7/310[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m31s[0m 105ms/step - accuracy: 0.3875 - loss: 1.5200
[36m(train_cnn_ray_tune pid=1160681)[0m 
[1m  8/310[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m31s[0m 103ms/step - accuracy: 0.3874 - loss: 1.5291
[1m  9/310[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m30s[0m 102ms/step - accuracy: 0.3872 - loss: 1.5367
[36m(train_cnn_ray_tune pid=1160681)[0m 
[1m 10/310[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m30s[0m 100ms/step - accuracy: 0.3872 - loss: 1.5431
[36m(train_cnn_ray_tune pid=1160681)[0m 
[1m 11/310[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m30s[0m 101ms/step - accuracy: 0.3869 - loss: 1.5491
[1m 12/310[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m29s[0m 99ms/step - accuracy: 0.3863 - loss: 1.5542 
[36m(train_cnn_ray_tune pid=1160675)[0m 
[1m  2/619[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:08[0m 111ms/step - accuracy: 0.3594 - loss: 2.1855
[1m  3/619[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m57s[0m 93ms/step - accuracy: 0.3715 - loss: 2.1683  
[36m(train_cnn_ray_tune pid=1160675)[0m 
[1m  1/619[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:16:00[0m 7s/step - accuracy: 0.3125 - loss: 2.2511[32m [repeated 14x across cluster][0m
[36m(train_cnn_ray_tune pid=1160678)[0m 
[1m 48/310[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m16s[0m 61ms/step - accuracy: 0.4699 - loss: 1.3063
[1m 49/310[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m15s[0m 61ms/step - accuracy: 0.4714 - loss: 1.3026[32m [repeated 203x across cluster][0m
[36m(train_cnn_ray_tune pid=1160657)[0m 
[1m134/619[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m19s[0m 40ms/step - accuracy: 0.4125 - loss: 1.5755[32m [repeated 309x across cluster][0m
[36m(train_cnn_ray_tune pid=1160659)[0m 
[1m  1/310[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m41:13[0m 8s/step - accuracy: 0.2500 - loss: 2.1492
[1m  2/310[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m34s[0m 114ms/step - accuracy: 0.2656 - loss: 2.1027[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=1160662)[0m 
[1m187/619[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m15s[0m 37ms/step - accuracy: 0.5106 - loss: 1.1905
[1m189/619[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m15s[0m 37ms/step - accuracy: 0.5111 - loss: 1.1891
[1m191/619[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m15s[0m 37ms/step - accuracy: 0.5115 - loss: 1.1878
[36m(train_cnn_ray_tune pid=1160661)[0m 
[1m 29/310[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m33s[0m 118ms/step - accuracy: 0.3384 - loss: 1.7304
[1m 30/310[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m32s[0m 118ms/step - accuracy: 0.3414 - loss: 1.7240[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=1160661)[0m 
[1m 33/310[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m32s[0m 118ms/step - accuracy: 0.3492 - loss: 1.7076[32m [repeated 30x across cluster][0m
[36m(train_cnn_ray_tune pid=1160679)[0m 
[1m117/310[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m9s[0m 51ms/step - accuracy: 0.3711 - loss: 1.5546
[36m(train_cnn_ray_tune pid=1160679)[0m 
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[36m(train_cnn_ray_tune pid=1160679)[0m 
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[36m(train_cnn_ray_tune pid=1160679)[0m 
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[36m(train_cnn_ray_tune pid=1160679)[0m 
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[36m(train_cnn_ray_tune pid=1160679)[0m 
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[36m(train_cnn_ray_tune pid=1160679)[0m 
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[36m(train_cnn_ray_tune pid=1160679)[0m 
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[36m(train_cnn_ray_tune pid=1160679)[0m 
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[36m(train_cnn_ray_tune pid=1160679)[0m 
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[36m(train_cnn_ray_tune pid=1160679)[0m 
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[36m(train_cnn_ray_tune pid=1160657)[0m 
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[36m(train_cnn_ray_tune pid=1160657)[0m 
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[36m(train_cnn_ray_tune pid=1160680)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160679)[0m 
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[36m(train_cnn_ray_tune pid=1160679)[0m 
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[36m(train_cnn_ray_tune pid=1160678)[0m 
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[36m(train_cnn_ray_tune pid=1160680)[0m 
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[36m(train_cnn_ray_tune pid=1160660)[0m 
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[36m(train_cnn_ray_tune pid=1160658)[0m 
[36m(train_cnn_ray_tune pid=1160658)[0m 
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[36m(train_cnn_ray_tune pid=1160676)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160669)[0m 
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[36m(train_cnn_ray_tune pid=1160679)[0m 
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[36m(train_cnn_ray_tune pid=1160658)[0m 
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[36m(train_cnn_ray_tune pid=1160680)[0m 
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[36m(train_cnn_ray_tune pid=1160657)[0m 
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Trial status: 20 RUNNING
Current time: 2025-11-08 16:40:27. 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_2e133    RUNNING            2   rmsprop         relu                                   16                 96                  3                 1          6.07697e-05         25 │
│ trial_2e133    RUNNING            3   adam            relu                                   32                 32                  5                 1          1.30564e-05         27 │
│ trial_2e133    RUNNING            3   adam            relu                                   32                 96                  3                 0          3.11011e-05         20 │
│ trial_2e133    RUNNING            2   adam            tanh                                   16                 96                  3                 1          0.00329571          17 │
│ trial_2e133    RUNNING            2   adam            tanh                                   32                 96                  3                 0          0.000165898         20 │
│ trial_2e133    RUNNING            3   rmsprop         relu                                   32                 64                  5                 1          0.00302727          18 │
│ trial_2e133    RUNNING            2   adam            relu                                   16                 32                  5                 0          8.69755e-05         18 │
│ trial_2e133    RUNNING            3   adam            relu                                   16                 32                  5                 0          0.000149645         28 │
│ trial_2e133    RUNNING            2   rmsprop         relu                                   16                 32                  5                 1          0.00234329          17 │
│ trial_2e133    RUNNING            2   rmsprop         tanh                                   16                 96                  5                 1          0.00031027          18 │
│ trial_2e133    RUNNING            2   adam            tanh                                   16                 64                  3                 0          0.000308353         26 │
│ trial_2e133    RUNNING            3   rmsprop         relu                                   32                 32                  5                 0          0.00111465          17 │
│ trial_2e133    RUNNING            2   rmsprop         tanh                                   16                 32                  5                 0          0.000595899         20 │
│ trial_2e133    RUNNING            2   adam            tanh                                   32                 32                  3                 0          0.000147791         24 │
│ trial_2e133    RUNNING            3   rmsprop         tanh                                   32                 64                  5                 0          2.69047e-05         23 │
│ trial_2e133    RUNNING            2   adam            relu                                   16                 32                  5                 0          0.000225987         26 │
│ trial_2e133    RUNNING            3   rmsprop         tanh                                   16                 64                  5                 0          4.29062e-05         17 │
│ trial_2e133    RUNNING            3   rmsprop         tanh                                   32                 64                  5                 0          0.00161065          19 │
│ trial_2e133    RUNNING            3   rmsprop         relu                                   16                 64                  3                 1          0.000108525         28 │
│ trial_2e133    RUNNING            2   rmsprop         relu                                   32                 96                  3                 1          1.93582e-05         23 │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=1160679)[0m 
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[36m(train_cnn_ray_tune pid=1160679)[0m Epoch 2/24
[36m(train_cnn_ray_tune pid=1160672)[0m 
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[36m(train_cnn_ray_tune pid=1160679)[0m 
[1m  1/310[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m50s[0m 162ms/step - accuracy: 0.3750 - loss: 1.1553
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[36m(train_cnn_ray_tune pid=1160676)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160657)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160662)[0m 
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[36m(train_cnn_ray_tune pid=1160677)[0m 
[1m  1/310[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:05[0m 212ms/step - accuracy: 0.6875 - loss: 0.9571
[36m(train_cnn_ray_tune pid=1160677)[0m 
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[36m(train_cnn_ray_tune pid=1160663)[0m 
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[36m(train_cnn_ray_tune pid=1160679)[0m 
[1m 54/310[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m13s[0m 55ms/step - accuracy: 0.5639 - loss: 0.9725[32m [repeated 229x across cluster][0m
[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160677)[0m 
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[36m(train_cnn_ray_tune pid=1160677)[0m Epoch 2/23
[36m(train_cnn_ray_tune pid=1160680)[0m 
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[36m(train_cnn_ray_tune pid=1160676)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160680)[0m 
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[36m(train_cnn_ray_tune pid=1160657)[0m 
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[36m(train_cnn_ray_tune pid=1160669)[0m 
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[36m(train_cnn_ray_tune pid=1160669)[0m Epoch 2/20
[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160669)[0m 
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[36m(train_cnn_ray_tune pid=1160671)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160669)[0m 
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[36m(train_cnn_ray_tune pid=1160669)[0m 
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[36m(train_cnn_ray_tune pid=1160677)[0m 
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[36m(train_cnn_ray_tune pid=1160659)[0m 
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[36m(train_cnn_ray_tune pid=1160658)[0m 
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[36m(train_cnn_ray_tune pid=1160674)[0m 
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[36m(train_cnn_ray_tune pid=1160679)[0m 
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[36m(train_cnn_ray_tune pid=1160679)[0m 
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[36m(train_cnn_ray_tune pid=1160674)[0m 
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[36m(train_cnn_ray_tune pid=1160674)[0m Epoch 2/19[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=1160663)[0m 
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[36m(train_cnn_ray_tune pid=1160671)[0m 
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[36m(train_cnn_ray_tune pid=1160681)[0m 
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[36m(train_cnn_ray_tune pid=1160673)[0m 
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[36m(train_cnn_ray_tune pid=1160674)[0m 
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[36m(train_cnn_ray_tune pid=1160675)[0m 
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[36m(train_cnn_ray_tune pid=1160672)[0m 
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[36m(train_cnn_ray_tune pid=1160681)[0m 
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[36m(train_cnn_ray_tune pid=1160669)[0m 
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[36m(train_cnn_ray_tune pid=1160680)[0m 
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[36m(train_cnn_ray_tune pid=1160657)[0m 
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[36m(train_cnn_ray_tune pid=1160669)[0m 
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[36m(train_cnn_ray_tune pid=1160675)[0m 
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[36m(train_cnn_ray_tune pid=1160676)[0m 
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[36m(train_cnn_ray_tune pid=1160676)[0m Epoch 2/26[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=1160658)[0m 
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[36m(train_cnn_ray_tune pid=1160662)[0m 
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[36m(train_cnn_ray_tune pid=1160662)[0m 
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[36m(train_cnn_ray_tune pid=1160657)[0m 
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[36m(train_cnn_ray_tune pid=1160669)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160679)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160676)[0m 
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[36m(train_cnn_ray_tune pid=1160659)[0m 
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[36m(train_cnn_ray_tune pid=1160663)[0m 
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[36m(train_cnn_ray_tune pid=1160679)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m Epoch 2/20[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1160677)[0m 
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[36m(train_cnn_ray_tune pid=1160681)[0m 
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[36m(train_cnn_ray_tune pid=1160662)[0m 
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[36m(train_cnn_ray_tune pid=1160657)[0m 
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[36m(train_cnn_ray_tune pid=1160660)[0m 
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[36m(train_cnn_ray_tune pid=1160671)[0m 
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[36m(train_cnn_ray_tune pid=1160671)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160674)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160678)[0m 
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[36m(train_cnn_ray_tune pid=1160659)[0m 
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[36m(train_cnn_ray_tune pid=1160671)[0m Epoch 2/18[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160671)[0m 
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[36m(train_cnn_ray_tune pid=1160676)[0m 
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Trial status: 20 RUNNING
Current time: 2025-11-08 16:40:58. Total running time: 1min 0s
Logical resource usage: 20.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status       N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     num_resblocks     tasa_aprendizaje     epochs │
├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_2e133    RUNNING            2   rmsprop         relu                                   16                 96                  3                 1          6.07697e-05         25 │
│ trial_2e133    RUNNING            3   adam            relu                                   32                 32                  5                 1          1.30564e-05         27 │
│ trial_2e133    RUNNING            3   adam            relu                                   32                 96                  3                 0          3.11011e-05         20 │
│ trial_2e133    RUNNING            2   adam            tanh                                   16                 96                  3                 1          0.00329571          17 │
│ trial_2e133    RUNNING            2   adam            tanh                                   32                 96                  3                 0          0.000165898         20 │
│ trial_2e133    RUNNING            3   rmsprop         relu                                   32                 64                  5                 1          0.00302727          18 │
│ trial_2e133    RUNNING            2   adam            relu                                   16                 32                  5                 0          8.69755e-05         18 │
│ trial_2e133    RUNNING            3   adam            relu                                   16                 32                  5                 0          0.000149645         28 │
│ trial_2e133    RUNNING            2   rmsprop         relu                                   16                 32                  5                 1          0.00234329          17 │
│ trial_2e133    RUNNING            2   rmsprop         tanh                                   16                 96                  5                 1          0.00031027          18 │
│ trial_2e133    RUNNING            2   adam            tanh                                   16                 64                  3                 0          0.000308353         26 │
│ trial_2e133    RUNNING            3   rmsprop         relu                                   32                 32                  5                 0          0.00111465          17 │
│ trial_2e133    RUNNING            2   rmsprop         tanh                                   16                 32                  5                 0          0.000595899         20 │
│ trial_2e133    RUNNING            2   adam            tanh                                   32                 32                  3                 0          0.000147791         24 │
│ trial_2e133    RUNNING            3   rmsprop         tanh                                   32                 64                  5                 0          2.69047e-05         23 │
│ trial_2e133    RUNNING            2   adam            relu                                   16                 32                  5                 0          0.000225987         26 │
│ trial_2e133    RUNNING            3   rmsprop         tanh                                   16                 64                  5                 0          4.29062e-05         17 │
│ trial_2e133    RUNNING            3   rmsprop         tanh                                   32                 64                  5                 0          0.00161065          19 │
│ trial_2e133    RUNNING            3   rmsprop         relu                                   16                 64                  3                 1          0.000108525         28 │
│ trial_2e133    RUNNING            2   rmsprop         relu                                   32                 96                  3                 1          1.93582e-05         23 │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=1160680)[0m 
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[36m(train_cnn_ray_tune pid=1160657)[0m 
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[36m(train_cnn_ray_tune pid=1160675)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160670)[0m �━[0m [1m7s[0m 82ms/step - accuracy: 0.6873 - loss: 0.7159
[36m(train_cnn_ray_tune pid=1160662)[0m 
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[36m(train_cnn_ray_tune pid=1160672)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160662)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160678)[0m 
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[36m(train_cnn_ray_tune pid=1160663)[0m 
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[36m(train_cnn_ray_tune pid=1160680)[0m 
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[36m(train_cnn_ray_tune pid=1160669)[0m 
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[36m(train_cnn_ray_tune pid=1160657)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160673)[0m 
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[36m(train_cnn_ray_tune pid=1160673)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160673)[0m 
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[36m(train_cnn_ray_tune pid=1160663)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160673)[0m Epoch 2/17[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=1160673)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160679)[0m 
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[36m(train_cnn_ray_tune pid=1160673)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160662)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160663)[0m 
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[36m(train_cnn_ray_tune pid=1160676)[0m 
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[36m(train_cnn_ray_tune pid=1160658)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160658)[0m 
[1m  1/619[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:17[0m 125ms/step - accuracy: 0.7500 - loss: 0.7495[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160670)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160679)[0m 
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[36m(train_cnn_ray_tune pid=1160657)[0m 
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[36m(train_cnn_ray_tune pid=1160670)[0m Epoch 3/18[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=1160670)[0m 
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[36m(train_cnn_ray_tune pid=1160677)[0m 
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[36m(train_cnn_ray_tune pid=1160662)[0m 
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[36m(train_cnn_ray_tune pid=1160658)[0m 
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[36m(train_cnn_ray_tune pid=1160675)[0m 
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[36m(train_cnn_ray_tune pid=1160679)[0m 
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[36m(train_cnn_ray_tune pid=1160680)[0m 
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[36m(train_cnn_ray_tune pid=1160678)[0m 
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[36m(train_cnn_ray_tune pid=1160657)[0m 
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[36m(train_cnn_ray_tune pid=1160680)[0m Epoch 3/26[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=1160680)[0m 
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[36m(train_cnn_ray_tune pid=1160662)[0m 
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[36m(train_cnn_ray_tune pid=1160674)[0m 
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[36m(train_cnn_ray_tune pid=1160676)[0m 
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[36m(train_cnn_ray_tune pid=1160659)[0m 
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[36m(train_cnn_ray_tune pid=1160660)[0m 
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[36m(train_cnn_ray_tune pid=1160679)[0m Epoch 5/24[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=1160679)[0m 
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[36m(train_cnn_ray_tune pid=1160679)[0m 
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[36m(train_cnn_ray_tune pid=1160663)[0m 
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[36m(train_cnn_ray_tune pid=1160669)[0m 
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[36m(train_cnn_ray_tune pid=1160678)[0m 
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[36m(train_cnn_ray_tune pid=1160662)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160658)[0m 
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[36m(train_cnn_ray_tune pid=1160677)[0m 
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[36m(train_cnn_ray_tune pid=1160679)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m Epoch 3/20[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=1160657)[0m 
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Trial status: 20 RUNNING
Current time: 2025-11-08 16:41:28. Total running time: 1min 30s
Logical resource usage: 20.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status       N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     num_resblocks     tasa_aprendizaje     epochs │
├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_2e133    RUNNING            2   rmsprop         relu                                   16                 96                  3                 1          6.07697e-05         25 │
│ trial_2e133    RUNNING            3   adam            relu                                   32                 32                  5                 1          1.30564e-05         27 │
│ trial_2e133    RUNNING            3   adam            relu                                   32                 96                  3                 0          3.11011e-05         20 │
│ trial_2e133    RUNNING            2   adam            tanh                                   16                 96                  3                 1          0.00329571          17 │
│ trial_2e133    RUNNING            2   adam            tanh                                   32                 96                  3                 0          0.000165898         20 │
│ trial_2e133    RUNNING            3   rmsprop         relu                                   32                 64                  5                 1          0.00302727          18 │
│ trial_2e133    RUNNING            2   adam            relu                                   16                 32                  5                 0          8.69755e-05         18 │
│ trial_2e133    RUNNING            3   adam            relu                                   16                 32                  5                 0          0.000149645         28 │
│ trial_2e133    RUNNING            2   rmsprop         relu                                   16                 32                  5                 1          0.00234329          17 │
│ trial_2e133    RUNNING            2   rmsprop         tanh                                   16                 96                  5                 1          0.00031027          18 │
│ trial_2e133    RUNNING            2   adam            tanh                                   16                 64                  3                 0          0.000308353         26 │
│ trial_2e133    RUNNING            3   rmsprop         relu                                   32                 32                  5                 0          0.00111465          17 │
│ trial_2e133    RUNNING            2   rmsprop         tanh                                   16                 32                  5                 0          0.000595899         20 │
│ trial_2e133    RUNNING            2   adam            tanh                                   32                 32                  3                 0          0.000147791         24 │
│ trial_2e133    RUNNING            3   rmsprop         tanh                                   32                 64                  5                 0          2.69047e-05         23 │
│ trial_2e133    RUNNING            2   adam            relu                                   16                 32                  5                 0          0.000225987         26 │
│ trial_2e133    RUNNING            3   rmsprop         tanh                                   16                 64                  5                 0          4.29062e-05         17 │
│ trial_2e133    RUNNING            3   rmsprop         tanh                                   32                 64                  5                 0          0.00161065          19 │
│ trial_2e133    RUNNING            3   rmsprop         relu                                   16                 64                  3                 1          0.000108525         28 │
│ trial_2e133    RUNNING            2   rmsprop         relu                                   32                 96                  3                 1          1.93582e-05         23 │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=1160669)[0m 
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[36m(train_cnn_ray_tune pid=1160657)[0m 
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[36m(train_cnn_ray_tune pid=1160659)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160662)[0m 
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[36m(train_cnn_ray_tune pid=1160662)[0m 
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[36m(train_cnn_ray_tune pid=1160669)[0m 
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[36m(train_cnn_ray_tune pid=1160659)[0m Epoch 4/27
[36m(train_cnn_ray_tune pid=1160659)[0m 
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[36m(train_cnn_ray_tune pid=1160680)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160657)[0m 
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[36m(train_cnn_ray_tune pid=1160676)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160677)[0m 
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[36m(train_cnn_ray_tune pid=1160677)[0m Epoch 5/23
[36m(train_cnn_ray_tune pid=1160677)[0m 
[1m  1/310[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m44s[0m 143ms/step - accuracy: 0.5938 - loss: 0.9681
[36m(train_cnn_ray_tune pid=1160669)[0m 
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[1m215/310[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m6s[0m 65ms/step - accuracy: 0.8054 - loss: 0.5086
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[36m(train_cnn_ray_tune pid=1160676)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160660)[0m 
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[1m  4/619[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m37s[0m 61ms/step - accuracy: 0.6263 - loss: 0.6541
[36m(train_cnn_ray_tune pid=1160670)[0m 
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[36m(train_cnn_ray_tune pid=1160663)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160662)[0m 
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[36m(train_cnn_ray_tune pid=1160674)[0m 
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[36m(train_cnn_ray_tune pid=1160679)[0m 
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[36m(train_cnn_ray_tune pid=1160679)[0m Epoch 6/24[32m [repeated 4x across cluster][0m
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160680)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160675)[0m 
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[36m(train_cnn_ray_tune pid=1160676)[0m 
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[36m(train_cnn_ray_tune pid=1160680)[0m 
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[36m(train_cnn_ray_tune pid=1160681)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160657)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160669)[0m 
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[36m(train_cnn_ray_tune pid=1160669)[0m Epoch 5/20[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=1160681)[0m 
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[36m(train_cnn_ray_tune pid=1160676)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160680)[0m 
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[36m(train_cnn_ray_tune pid=1160677)[0m 
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[36m(train_cnn_ray_tune pid=1160679)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160669)[0m 
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[36m(train_cnn_ray_tune pid=1160662)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160678)[0m 
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[36m(train_cnn_ray_tune pid=1160678)[0m Epoch 5/17[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=1160680)[0m 
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[36m(train_cnn_ray_tune pid=1160669)[0m 
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[36m(train_cnn_ray_tune pid=1160672)[0m 
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[36m(train_cnn_ray_tune pid=1160677)[0m 
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[36m(train_cnn_ray_tune pid=1160657)[0m 
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[36m(train_cnn_ray_tune pid=1160679)[0m 
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[36m(train_cnn_ray_tune pid=1160663)[0m 
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[36m(train_cnn_ray_tune pid=1160669)[0m 
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[36m(train_cnn_ray_tune pid=1160680)[0m 
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[36m(train_cnn_ray_tune pid=1160671)[0m 
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[36m(train_cnn_ray_tune pid=1160659)[0m 
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[36m(train_cnn_ray_tune pid=1160663)[0m 
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[36m(train_cnn_ray_tune pid=1160663)[0m Epoch 3/28[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1160675)[0m 
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[36m(train_cnn_ray_tune pid=1160676)[0m 
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[36m(train_cnn_ray_tune pid=1160677)[0m 
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[36m(train_cnn_ray_tune pid=1160657)[0m 
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[36m(train_cnn_ray_tune pid=1160662)[0m 
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[36m(train_cnn_ray_tune pid=1160662)[0m 
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Trial status: 20 RUNNING
Current time: 2025-11-08 16:41:58. Total running time: 2min 0s
Logical resource usage: 20.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status       N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     num_resblocks     tasa_aprendizaje     epochs │
├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_2e133    RUNNING            2   rmsprop         relu                                   16                 96                  3                 1          6.07697e-05         25 │
│ trial_2e133    RUNNING            3   adam            relu                                   32                 32                  5                 1          1.30564e-05         27 │
│ trial_2e133    RUNNING            3   adam            relu                                   32                 96                  3                 0          3.11011e-05         20 │
│ trial_2e133    RUNNING            2   adam            tanh                                   16                 96                  3                 1          0.00329571          17 │
│ trial_2e133    RUNNING            2   adam            tanh                                   32                 96                  3                 0          0.000165898         20 │
│ trial_2e133    RUNNING            3   rmsprop         relu                                   32                 64                  5                 1          0.00302727          18 │
│ trial_2e133    RUNNING            2   adam            relu                                   16                 32                  5                 0          8.69755e-05         18 │
│ trial_2e133    RUNNING            3   adam            relu                                   16                 32                  5                 0          0.000149645         28 │
│ trial_2e133    RUNNING            2   rmsprop         relu                                   16                 32                  5                 1          0.00234329          17 │
│ trial_2e133    RUNNING            2   rmsprop         tanh                                   16                 96                  5                 1          0.00031027          18 │
│ trial_2e133    RUNNING            2   adam            tanh                                   16                 64                  3                 0          0.000308353         26 │
│ trial_2e133    RUNNING            3   rmsprop         relu                                   32                 32                  5                 0          0.00111465          17 │
│ trial_2e133    RUNNING            2   rmsprop         tanh                                   16                 32                  5                 0          0.000595899         20 │
│ trial_2e133    RUNNING            2   adam            tanh                                   32                 32                  3                 0          0.000147791         24 │
│ trial_2e133    RUNNING            3   rmsprop         tanh                                   32                 64                  5                 0          2.69047e-05         23 │
│ trial_2e133    RUNNING            2   adam            relu                                   16                 32                  5                 0          0.000225987         26 │
│ trial_2e133    RUNNING            3   rmsprop         tanh                                   16                 64                  5                 0          4.29062e-05         17 │
│ trial_2e133    RUNNING            3   rmsprop         tanh                                   32                 64                  5                 0          0.00161065          19 │
│ trial_2e133    RUNNING            3   rmsprop         relu                                   16                 64                  3                 1          0.000108525         28 │
│ trial_2e133    RUNNING            2   rmsprop         relu                                   32                 96                  3                 1          1.93582e-05         23 │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=1160674)[0m 
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[36m(train_cnn_ray_tune pid=1160680)[0m 
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[36m(train_cnn_ray_tune pid=1160679)[0m 
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[36m(train_cnn_ray_tune pid=1160673)[0m 
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[36m(train_cnn_ray_tune pid=1160673)[0m Epoch 3/17[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=1160672)[0m 
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[36m(train_cnn_ray_tune pid=1160679)[0m 
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[36m(train_cnn_ray_tune pid=1160662)[0m 
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[36m(train_cnn_ray_tune pid=1160660)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160674)[0m 
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[36m(train_cnn_ray_tune pid=1160669)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160675)[0m 
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[36m(train_cnn_ray_tune pid=1160673)[0m 
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[36m(train_cnn_ray_tune pid=1160657)[0m 
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[36m(train_cnn_ray_tune pid=1160657)[0m 
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[36m(train_cnn_ray_tune pid=1160657)[0m Epoch 4/25[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1160671)[0m 
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[36m(train_cnn_ray_tune pid=1160678)[0m 
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[36m(train_cnn_ray_tune pid=1160669)[0m 
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[36m(train_cnn_ray_tune pid=1160679)[0m 
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[36m(train_cnn_ray_tune pid=1160672)[0m 
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[36m(train_cnn_ray_tune pid=1160679)[0m 
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[36m(train_cnn_ray_tune pid=1160669)[0m 
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[36m(train_cnn_ray_tune pid=1160675)[0m 
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[36m(train_cnn_ray_tune pid=1160670)[0m 
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[36m(train_cnn_ray_tune pid=1160662)[0m 
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[36m(train_cnn_ray_tune pid=1160657)[0m 
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[36m(train_cnn_ray_tune pid=1160662)[0m 
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[36m(train_cnn_ray_tune pid=1160662)[0m Epoch 5/17[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160658)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160658)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160681)[0m 
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[36m(train_cnn_ray_tune pid=1160672)[0m 
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[36m(train_cnn_ray_tune pid=1160680)[0m 
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[36m(train_cnn_ray_tune pid=1160669)[0m 
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[36m(train_cnn_ray_tune pid=1160679)[0m 
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[36m(train_cnn_ray_tune pid=1160662)[0m 
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[36m(train_cnn_ray_tune pid=1160657)[0m 
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[36m(train_cnn_ray_tune pid=1160679)[0m Epoch 8/24[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160679)[0m 
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[36m(train_cnn_ray_tune pid=1160676)[0m 
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[36m(train_cnn_ray_tune pid=1160679)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160660)[0m 
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[36m(train_cnn_ray_tune pid=1160672)[0m 
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[36m(train_cnn_ray_tune pid=1160676)[0m 
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[36m(train_cnn_ray_tune pid=1160669)[0m 
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[36m(train_cnn_ray_tune pid=1160672)[0m 
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[36m(train_cnn_ray_tune pid=1160673)[0m 
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[36m(train_cnn_ray_tune pid=1160670)[0m 
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[36m(train_cnn_ray_tune pid=1160680)[0m Epoch 5/26[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160676)[0m 
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[36m(train_cnn_ray_tune pid=1160680)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160663)[0m 
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[36m(train_cnn_ray_tune pid=1160680)[0m 
[1m  1/619[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:45[0m 171ms/step - accuracy: 0.6875 - loss: 0.8234
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[36m(train_cnn_ray_tune pid=1160669)[0m 
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[36m(train_cnn_ray_tune pid=1160669)[0m 
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[36m(train_cnn_ray_tune pid=1160674)[0m 
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[36m(train_cnn_ray_tune pid=1160663)[0m 
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[36m(train_cnn_ray_tune pid=1160662)[0m 
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[36m(train_cnn_ray_tune pid=1160659)[0m 
[1m114/310[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m13s[0m 71ms/step - accuracy: 0.5470 - loss: 1.1300[32m [repeated 355x across cluster][0m
[36m(train_cnn_ray_tune pid=1160675)[0m 
[1m  1/619[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:43[0m 167ms/step - accuracy: 0.6875 - loss: 0.6349
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[36m(train_cnn_ray_tune pid=1160675)[0m Epoch 4/28
[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160662)[0m 
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[36m(train_cnn_ray_tune pid=1160675)[0m 
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[36m(train_cnn_ray_tune pid=1160675)[0m 
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[36m(train_cnn_ray_tune pid=1160669)[0m 
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[36m(train_cnn_ray_tune pid=1160669)[0m Epoch 7/20
[36m(train_cnn_ray_tune pid=1160669)[0m 
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[36m(train_cnn_ray_tune pid=1160669)[0m 
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[36m(train_cnn_ray_tune pid=1160662)[0m 
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[36m(train_cnn_ray_tune pid=1160663)[0m 
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[36m(train_cnn_ray_tune pid=1160660)[0m 
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[36m(train_cnn_ray_tune pid=1160659)[0m 
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Trial status: 20 RUNNING
Current time: 2025-11-08 16:42:28. Total running time: 2min 30s
Logical resource usage: 20.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status       N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     num_resblocks     tasa_aprendizaje     epochs │
├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_2e133    RUNNING            2   rmsprop         relu                                   16                 96                  3                 1          6.07697e-05         25 │
│ trial_2e133    RUNNING            3   adam            relu                                   32                 32                  5                 1          1.30564e-05         27 │
│ trial_2e133    RUNNING            3   adam            relu                                   32                 96                  3                 0          3.11011e-05         20 │
│ trial_2e133    RUNNING            2   adam            tanh                                   16                 96                  3                 1          0.00329571          17 │
│ trial_2e133    RUNNING            2   adam            tanh                                   32                 96                  3                 0          0.000165898         20 │
│ trial_2e133    RUNNING            3   rmsprop         relu                                   32                 64                  5                 1          0.00302727          18 │
│ trial_2e133    RUNNING            2   adam            relu                                   16                 32                  5                 0          8.69755e-05         18 │
│ trial_2e133    RUNNING            3   adam            relu                                   16                 32                  5                 0          0.000149645         28 │
│ trial_2e133    RUNNING            2   rmsprop         relu                                   16                 32                  5                 1          0.00234329          17 │
│ trial_2e133    RUNNING            2   rmsprop         tanh                                   16                 96                  5                 1          0.00031027          18 │
│ trial_2e133    RUNNING            2   adam            tanh                                   16                 64                  3                 0          0.000308353         26 │
│ trial_2e133    RUNNING            3   rmsprop         relu                                   32                 32                  5                 0          0.00111465          17 │
│ trial_2e133    RUNNING            2   rmsprop         tanh                                   16                 32                  5                 0          0.000595899         20 │
│ trial_2e133    RUNNING            2   adam            tanh                                   32                 32                  3                 0          0.000147791         24 │
│ trial_2e133    RUNNING            3   rmsprop         tanh                                   32                 64                  5                 0          2.69047e-05         23 │
│ trial_2e133    RUNNING            2   adam            relu                                   16                 32                  5                 0          0.000225987         26 │
│ trial_2e133    RUNNING            3   rmsprop         tanh                                   16                 64                  5                 0          4.29062e-05         17 │
│ trial_2e133    RUNNING            3   rmsprop         tanh                                   32                 64                  5                 0          0.00161065          19 │
│ trial_2e133    RUNNING            3   rmsprop         relu                                   16                 64                  3                 1          0.000108525         28 │
│ trial_2e133    RUNNING            2   rmsprop         relu                                   32                 96                  3                 1          1.93582e-05         23 │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=1160678)[0m 
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[36m(train_cnn_ray_tune pid=1160671)[0m 
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[36m(train_cnn_ray_tune pid=1160669)[0m 
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[36m(train_cnn_ray_tune pid=1160657)[0m 
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[36m(train_cnn_ray_tune pid=1160678)[0m 
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[36m(train_cnn_ray_tune pid=1160658)[0m 
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[36m(train_cnn_ray_tune pid=1160658)[0m 
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[36m(train_cnn_ray_tune pid=1160679)[0m 
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[36m(train_cnn_ray_tune pid=1160679)[0m Epoch 9/24[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=1160679)[0m 
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[36m(train_cnn_ray_tune pid=1160679)[0m 
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[36m(train_cnn_ray_tune pid=1160670)[0m 
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[36m(train_cnn_ray_tune pid=1160657)[0m 
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[36m(train_cnn_ray_tune pid=1160675)[0m 
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[36m(train_cnn_ray_tune pid=1160676)[0m 
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[36m(train_cnn_ray_tune pid=1160680)[0m 
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[36m(train_cnn_ray_tune pid=1160669)[0m 
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[36m(train_cnn_ray_tune pid=1160680)[0m 
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[36m(train_cnn_ray_tune pid=1160674)[0m 
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[36m(train_cnn_ray_tune pid=1160663)[0m 
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[36m(train_cnn_ray_tune pid=1160674)[0m 
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[36m(train_cnn_ray_tune pid=1160674)[0m Epoch 6/19
[36m(train_cnn_ray_tune pid=1160674)[0m 
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[36m(train_cnn_ray_tune pid=1160677)[0m 
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[36m(train_cnn_ray_tune pid=1160677)[0m Epoch 8/23
[36m(train_cnn_ray_tune pid=1160677)[0m 
[1m  1/310[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m35s[0m 114ms/step - accuracy: 0.5938 - loss: 0.9109
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[36m(train_cnn_ray_tune pid=1160657)[0m 
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[36m(train_cnn_ray_tune pid=1160669)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160675)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160660)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160659)[0m 
[1m  1/310[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m46s[0m 151ms/step - accuracy: 0.6250 - loss: 0.9165
[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160658)[0m 
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[36m(train_cnn_ray_tune pid=1160680)[0m 
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[1m504/619[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m4s[0m 43ms/step - accuracy: 0.7283 - loss: 0.6820
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[36m(train_cnn_ray_tune pid=1160660)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160658)[0m 
[1m619/619[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m31s[0m 51ms/step - accuracy: 0.7181 - loss: 0.6524 - val_accuracy: 0.6622 - val_loss: 0.7497[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=1160658)[0m Epoch 6/20[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=1160662)[0m 
[1m  1/619[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:40[0m 163ms/step - accuracy: 0.8125 - loss: 0.7202
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[36m(train_cnn_ray_tune pid=1160669)[0m 
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[36m(train_cnn_ray_tune pid=1160679)[0m 
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[36m(train_cnn_ray_tune pid=1160671)[0m 
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[36m(train_cnn_ray_tune pid=1160663)[0m 
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[36m(train_cnn_ray_tune pid=1160669)[0m 
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[36m(train_cnn_ray_tune pid=1160669)[0m 
[1m2
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160657)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160675)[0m 
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[36m(train_cnn_ray_tune pid=1160677)[0m 
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[36m(train_cnn_ray_tune pid=1160657)[0m 
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[36m(train_cnn_ray_tune pid=1160657)[0m Epoch 5/25[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1160660)[0m 
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[36m(train_cnn_ray_tune pid=1160678)[0m 
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[36m(train_cnn_ray_tune pid=1160679)[0m 
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[36m(train_cnn_ray_tune pid=1160680)[0m 
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[36m(train_cnn_ray_tune pid=1160675)[0m 
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[36m(train_cnn_ray_tune pid=1160659)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160662)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160669)[0m 
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[36m(train_cnn_ray_tune pid=1160669)[0m Epoch 8/20[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160675)[0m 
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[36m(train_cnn_ray_tune pid=1160675)[0m 
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[36m(train_cnn_ray_tune pid=1160673)[0m 
[1m  1/619[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:49[0m 176ms/step - accuracy: 0.6875 - loss: 0.7573
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160670)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160669)[0m 
[1m  3/310[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m18s[0m 59ms/step - accuracy: 0.8733 - loss: 0.3132  
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[36m(train_cnn_ray_tune pid=1160657)[0m 
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[36m(train_cnn_ray_tune pid=1160658)[0m 
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[36m(train_cnn_ray_tune pid=1160678)[0m 
[1m  1/310[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m52s[0m 168ms/step - accuracy: 0.8750 - loss: 0.3586
[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160660)[0m 
[1m  1/619[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:26[0m 140ms/step - accuracy: 0.7500 - loss: 0.5647
[36m(train_cnn_ray_tune pid=1160660)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160662)[0m 
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[36m(train_cnn_ray_tune pid=1160660)[0m 
[1m619/619[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m40s[0m 64ms/step - accuracy: 0.7131 - loss: 0.6544 - val_accuracy: 0.6900 - val_loss: 0.6508[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1160660)[0m Epoch 5/17[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1160662)[0m 
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[36m(train_cnn_ray_tune pid=1160679)[0m 
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[36m(train_cnn_ray_tune pid=1160662)[0m 
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Trial status: 20 RUNNING
Current time: 2025-11-08 16:42:58. Total running time: 3min 0s
Logical resource usage: 20.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status       N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     num_resblocks     tasa_aprendizaje     epochs │
├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_2e133    RUNNING            2   rmsprop         relu                                   16                 96                  3                 1          6.07697e-05         25 │
│ trial_2e133    RUNNING            3   adam            relu                                   32                 32                  5                 1          1.30564e-05         27 │
│ trial_2e133    RUNNING            3   adam            relu                                   32                 96                  3                 0          3.11011e-05         20 │
│ trial_2e133    RUNNING            2   adam            tanh                                   16                 96                  3                 1          0.00329571          17 │
│ trial_2e133    RUNNING            2   adam            tanh                                   32                 96                  3                 0          0.000165898         20 │
│ trial_2e133    RUNNING            3   rmsprop         relu                                   32                 64                  5                 1          0.00302727          18 │
│ trial_2e133    RUNNING            2   adam            relu                                   16                 32                  5                 0          8.69755e-05         18 │
│ trial_2e133    RUNNING            3   adam            relu                                   16                 32                  5                 0          0.000149645         28 │
│ trial_2e133    RUNNING            2   rmsprop         relu                                   16                 32                  5                 1          0.00234329          17 │
│ trial_2e133    RUNNING            2   rmsprop         tanh                                   16                 96                  5                 1          0.00031027          18 │
│ trial_2e133    RUNNING            2   adam            tanh                                   16                 64                  3                 0          0.000308353         26 │
│ trial_2e133    RUNNING            3   rmsprop         relu                                   32                 32                  5                 0          0.00111465          17 │
│ trial_2e133    RUNNING            2   rmsprop         tanh                                   16                 32                  5                 0          0.000595899         20 │
│ trial_2e133    RUNNING            2   adam            tanh                                   32                 32                  3                 0          0.000147791         24 │
│ trial_2e133    RUNNING            3   rmsprop         tanh                                   32                 64                  5                 0          2.69047e-05         23 │
│ trial_2e133    RUNNING            2   adam            relu                                   16                 32                  5                 0          0.000225987         26 │
│ trial_2e133    RUNNING            3   rmsprop         tanh                                   16                 64                  5                 0          4.29062e-05         17 │
│ trial_2e133    RUNNING            3   rmsprop         tanh                                   32                 64                  5                 0          0.00161065          19 │
│ trial_2e133    RUNNING            3   rmsprop         relu                                   16                 64                  3                 1          0.000108525         28 │
│ trial_2e133    RUNNING            2   rmsprop         relu                                   32                 96                  3                 1          1.93582e-05         23 │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=1160671)[0m 
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[36m(train_cnn_ray_tune pid=1160669)[0m 
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[36m(train_cnn_ray_tune pid=1160657)[0m 
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[36m(train_cnn_ray_tune pid=1160662)[0m 
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[36m(train_cnn_ray_tune pid=1160677)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160677)[0m 
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[36m(train_cnn_ray_tune pid=1160677)[0m Epoch 9/23
[36m(train_cnn_ray_tune pid=1160658)[0m 
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[36m(train_cnn_ray_tune pid=1160658)[0m 
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[36m(train_cnn_ray_tune pid=1160669)[0m 
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[36m(train_cnn_ray_tune pid=1160662)[0m 
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[36m(train_cnn_ray_tune pid=1160659)[0m 
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[36m(train_cnn_ray_tune pid=1160659)[0m Epoch 8/27
[36m(train_cnn_ray_tune pid=1160659)[0m 
[1m  1/310[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m49s[0m 161ms/step - accuracy: 0.6562 - loss: 1.0493
[36m(train_cnn_ray_tune pid=1160663)[0m 
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[36m(train_cnn_ray_tune pid=1160657)[0m 
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[36m(train_cnn_ray_tune pid=1160676)[0m 
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[36m(train_cnn_ray_tune pid=1160677)[0m 
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[36m(train_cnn_ray_tune pid=1160674)[0m 
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[36m(train_cnn_ray_tune pid=1160659)[0m 
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[36m(train_cnn_ray_tune pid=1160680)[0m 
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[36m(train_cnn_ray_tune pid=1160677)[0m 
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[36m(train_cnn_ray_tune pid=1160669)[0m 
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[36m(train_cnn_ray_tune pid=1160670)[0m 
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[36m(train_cnn_ray_tune pid=1160670)[0m Epoch 7/18[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=1160675)[0m 
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[36m(train_cnn_ray_tune pid=1160676)[0m 
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[36m(train_cnn_ray_tune pid=1160675)[0m 
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[36m(train_cnn_ray_tune pid=1160681)[0m 
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[36m(train_cnn_ray_tune pid=1160671)[0m 
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[36m(train_cnn_ray_tune pid=1160672)[0m 
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[36m(train_cnn_ray_tune pid=1160680)[0m 
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[36m(train_cnn_ray_tune pid=1160669)[0m 
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[36m(train_cnn_ray_tune pid=1160671)[0m Epoch 5/18[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160663)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160663)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160663)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160659)[0m 
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[36m(train_cnn_ray_tune pid=1160679)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160672)[0m 
[1m619/619[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m31s[0m 51ms/step - accuracy: 0.6650 - loss: 0.8020 - val_accuracy: 0.6327 - val_loss: 0.8679[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=1160672)[0m Epoch 7/18[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
[1m  1/310[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:03[0m 206ms/step - accuracy: 0.6562 - loss: 0.7221
[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160662)[0m 
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[36m(train_cnn_ray_tune pid=1160670)[0m 
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[36m(train_cnn_ray_tune pid=1160680)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160680)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160670)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160679)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160659)[0m 
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[36m(train_cnn_ray_tune pid=1160663)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160657)[0m 
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[36m(train_cnn_ray_tune pid=1160657)[0m Epoch 6/25[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160680)[0m 
[1m 99/619[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m23s[0m 45ms/step - accuracy: 0.7842 - loss: 0.5747
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160680)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160681)[0m 
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[36m(train_cnn_ray_tune pid=1160657)[0m 
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[36m(train_cnn_ray_tune pid=1160657)[0m 
[1m  1/619[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:24[0m 137ms/step - accuracy: 0.6875 - loss: 0.6907[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160657)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160676)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160662)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160680)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160659)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160678)[0m 
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[36m(train_cnn_ray_tune pid=1160662)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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Trial status: 20 RUNNING
Current time: 2025-11-08 16:43:28. Total running time: 3min 30s
Logical resource usage: 20.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status       N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     num_resblocks     tasa_aprendizaje     epochs │
├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_2e133    RUNNING            2   rmsprop         relu                                   16                 96                  3                 1          6.07697e-05         25 │
│ trial_2e133    RUNNING            3   adam            relu                                   32                 32                  5                 1          1.30564e-05         27 │
│ trial_2e133    RUNNING            3   adam            relu                                   32                 96                  3                 0          3.11011e-05         20 │
│ trial_2e133    RUNNING            2   adam            tanh                                   16                 96                  3                 1          0.00329571          17 │
│ trial_2e133    RUNNING            2   adam            tanh                                   32                 96                  3                 0          0.000165898         20 │
│ trial_2e133    RUNNING            3   rmsprop         relu                                   32                 64                  5                 1          0.00302727          18 │
│ trial_2e133    RUNNING            2   adam            relu                                   16                 32                  5                 0          8.69755e-05         18 │
│ trial_2e133    RUNNING            3   adam            relu                                   16                 32                  5                 0          0.000149645         28 │
│ trial_2e133    RUNNING            2   rmsprop         relu                                   16                 32                  5                 1          0.00234329          17 │
│ trial_2e133    RUNNING            2   rmsprop         tanh                                   16                 96                  5                 1          0.00031027          18 │
│ trial_2e133    RUNNING            2   adam            tanh                                   16                 64                  3                 0          0.000308353         26 │
│ trial_2e133    RUNNING            3   rmsprop         relu                                   32                 32                  5                 0          0.00111465          17 │
│ trial_2e133    RUNNING            2   rmsprop         tanh                                   16                 32                  5                 0          0.000595899         20 │
│ trial_2e133    RUNNING            2   adam            tanh                                   32                 32                  3                 0          0.000147791         24 │
│ trial_2e133    RUNNING            3   rmsprop         tanh                                   32                 64                  5                 0          2.69047e-05         23 │
│ trial_2e133    RUNNING            2   adam            relu                                   16                 32                  5                 0          0.000225987         26 │
│ trial_2e133    RUNNING            3   rmsprop         tanh                                   16                 64                  5                 0          4.29062e-05         17 │
│ trial_2e133    RUNNING            3   rmsprop         tanh                                   32                 64                  5                 0          0.00161065          19 │
│ trial_2e133    RUNNING            3   rmsprop         relu                                   16                 64                  3                 1          0.000108525         28 │
│ trial_2e133    RUNNING            2   rmsprop         relu                                   32                 96                  3                 1          1.93582e-05         23 │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160659)[0m 
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[36m(train_cnn_ray_tune pid=1160659)[0m Epoch 9/27
[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160675)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160680)[0m 
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[36m(train_cnn_ray_tune pid=1160657)[0m 
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[36m(train_cnn_ray_tune pid=1160659)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160679)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160669)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160669)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160669)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160675)[0m 
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[36m(train_cnn_ray_tune pid=1160662)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160669)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160669)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160669)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160669)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160669)[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=1160669)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=1160662)[0m 2025-11-08 16:40:01.410228: 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=1160680)[0m 2025-11-08 16:40:01.446725: 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=1160662)[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=1160662)[0m E0000 00:00:1762616401.462646 1161949 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=1160662)[0m E0000 00:00:1762616401.471407 1161949 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=1160662)[0m W0000 00:00:1762616401.492783 1161949 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=1160662)[0m 2025-11-08 16:40:01.498890: 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=1160662)[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=1160662)[0m 2025-11-08 16:40:04.797221: 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=1160662)[0m 2025-11-08 16:40:04.797298: 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=1160662)[0m 2025-11-08 16:40:04.797308: 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=1160662)[0m 2025-11-08 16:40:04.797315: 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=1160662)[0m 2025-11-08 16:40:04.797320: 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=1160662)[0m 2025-11-08 16:40:04.797324: 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=1160662)[0m 2025-11-08 16:40:04.797695: 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=1160662)[0m 2025-11-08 16:40:04.797734: 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=1160662)[0m 2025-11-08 16:40:04.797739: 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=1160669)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160674)[0m 
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[36m(train_cnn_ray_tune pid=1160674)[0m Epoch 8/19
[36m(train_cnn_ray_tune pid=1160669)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160674)[0m 
[1m  1/310[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m48s[0m 156ms/step - accuracy: 0.8438 - loss: 0.4271
[36m(train_cnn_ray_tune pid=1160663)[0m 
[1m  1/619[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:25[0m 138ms/step - accuracy: 0.7500 - loss: 1.1193
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[36m(train_cnn_ray_tune pid=1160669)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160674)[0m 
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[36m(train_cnn_ray_tune pid=1160669)[0m 
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[36m(train_cnn_ray_tune pid=1160669)[0m 
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[36m(train_cnn_ray_tune pid=1160669)[0m 
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[36m(train_cnn_ray_tune pid=1160669)[0m 
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[36m(train_cnn_ray_tune pid=1160663)[0m 
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[36m(train_cnn_ray_tune pid=1160657)[0m 
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[36m(train_cnn_ray_tune pid=1160669)[0m 
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Trial trial_2e133 finished iteration 1 at 2025-11-08 16:43:34. Total running time: 3min 37s
╭──────────────────────────────────────╮
│ Trial trial_2e133 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             214.247 │
│ time_total_s                 214.247 │
│ training_iteration                 1 │
│ val_accuracy                  0.6875 │
╰──────────────────────────────────────╯

Trial trial_2e133 completed after 1 iterations at 2025-11-08 16:43:34. Total running time: 3min 37s
[36m(train_cnn_ray_tune pid=1160662)[0m 
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[36m(train_cnn_ray_tune pid=1160670)[0m 
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[36m(train_cnn_ray_tune pid=1160670)[0m loss: 0.3526
[36m(train_cnn_ray_tune pid=1160671)[0m 
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[36m(train_cnn_ray_tune pid=1160680)[0m 
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[36m(train_cnn_ray_tune pid=1160675)[0m 
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[36m(train_cnn_ray_tune pid=1160680)[0m 
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[36m(train_cnn_ray_tune pid=1160671)[0m 
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[36m(train_cnn_ray_tune pid=1160678)[0m 
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[36m(train_cnn_ray_tune pid=1160662)[0m 
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[36m(train_cnn_ray_tune pid=1160662)[0m 
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[36m(train_cnn_ray_tune pid=1160662)[0m Epoch 8/17[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=1160678)[0m 
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[36m(train_cnn_ray_tune pid=1160678)[0m 
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[36m(train_cnn_ray_tune pid=1160677)[0m 
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[36m(train_cnn_ray_tune pid=1160678)[0m 
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[36m(train_cnn_ray_tune pid=1160657)[0m 
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[36m(train_cnn_ray_tune pid=1160674)[0m 
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[36m(train_cnn_ray_tune pid=1160678)[0m 
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[36m(train_cnn_ray_tune pid=1160678)[0m 
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[36m(train_cnn_ray_tune pid=1160678)[0m 
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[36m(train_cnn_ray_tune pid=1160678)[0m 
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[36m(train_cnn_ray_tune pid=1160678)[0m 
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[36m(train_cnn_ray_tune pid=1160672)[0m 
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[36m(train_cnn_ray_tune pid=1160678)[0m 
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[36m(train_cnn_ray_tune pid=1160678)[0m 
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[36m(train_cnn_ray_tune pid=1160678)[0m 
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[36m(train_cnn_ray_tune pid=1160678)[0m 
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[36m(train_cnn_ray_tune pid=1160678)[0m 
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[36m(train_cnn_ray_tune pid=1160678)[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=1160678)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=1160678)[0m 
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[36m(train_cnn_ray_tune pid=1160678)[0m 
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[36m(train_cnn_ray_tune pid=1160663)[0m 
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[1m119/619[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m31s[0m 63ms/step - accuracy: 0.7333 - loss: 0.6887
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[36m(train_cnn_ray_tune pid=1160678)[0m 
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[36m(train_cnn_ray_tune pid=1160678)[0m 
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[36m(train_cnn_ray_tune pid=1160678)[0m 
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[36m(train_cnn_ray_tune pid=1160678)[0m 
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Trial trial_2e133 finished iteration 1 at 2025-11-08 16:43:42. Total running time: 3min 45s
╭──────────────────────────────────────╮
│ Trial trial_2e133 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             221.951 │
│ time_total_s                 221.951 │
│ training_iteration                 1 │
│ val_accuracy                 0.71383 │
╰──────────────────────────────────────╯

Trial trial_2e133 completed after 1 iterations at 2025-11-08 16:43:42. Total running time: 3min 45s
[36m(train_cnn_ray_tune pid=1160672)[0m 
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[36m(train_cnn_ray_tune pid=1160679)[0m 
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[36m(train_cnn_ray_tune pid=1160679)[0m 
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[36m(train_cnn_ray_tune pid=1160673)[0m 
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[36m(train_cnn_ray_tune pid=1160658)[0m 
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[36m(train_cnn_ray_tune pid=1160658)[0m Epoch 8/20[32m [repeated 3x across cluster][0m
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[36m(train_cnn_ray_tune pid=1160662)[0m 
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[36m(train_cnn_ray_tune pid=1160670)[0m 
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[36m(train_cnn_ray_tune pid=1160677)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160660)[0m 
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[36m(train_cnn_ray_tune pid=1160679)[0m 
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[36m(train_cnn_ray_tune pid=1160657)[0m 
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[36m(train_cnn_ray_tune pid=1160659)[0m 
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[36m(train_cnn_ray_tune pid=1160659)[0m Epoch 10/27[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=1160663)[0m 
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[36m(train_cnn_ray_tune pid=1160663)[0m 
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[36m(train_cnn_ray_tune pid=1160662)[0m 
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[36m(train_cnn_ray_tune pid=1160680)[0m 
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[36m(train_cnn_ray_tune pid=1160662)[0m 
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[36m(train_cnn_ray_tune pid=1160659)[0m 
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[36m(train_cnn_ray_tune pid=1160663)[0m 
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[36m(train_cnn_ray_tune pid=1160671)[0m 
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[36m(train_cnn_ray_tune pid=1160662)[0m 
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[36m(train_cnn_ray_tune pid=1160677)[0m 
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[36m(train_cnn_ray_tune pid=1160657)[0m 
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[36m(train_cnn_ray_tune pid=1160679)[0m 
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[36m(train_cnn_ray_tune pid=1160679)[0m Epoch 14/24[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1160662)[0m 
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[36m(train_cnn_ray_tune pid=1160675)[0m 
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[36m(train_cnn_ray_tune pid=1160659)[0m 
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[36m(train_cnn_ray_tune pid=1160673)[0m 
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[36m(train_cnn_ray_tune pid=1160660)[0m 
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[36m(train_cnn_ray_tune pid=1160662)[0m 
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[36m(train_cnn_ray_tune pid=1160679)[0m 
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[36m(train_cnn_ray_tune pid=1160675)[0m 
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[36m(train_cnn_ray_tune pid=1160679)[0m 
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[36m(train_cnn_ray_tune pid=1160659)[0m 
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Trial status: 18 RUNNING | 2 TERMINATED
Current time: 2025-11-08 16:43:58. Total running time: 4min 0s
Logical resource usage: 18.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     num_resblocks     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_2e133    RUNNING              2   rmsprop         relu                                   16                 96                  3                 1          6.07697e-05         25                                              │
│ trial_2e133    RUNNING              3   adam            relu                                   32                 32                  5                 1          1.30564e-05         27                                              │
│ trial_2e133    RUNNING              3   adam            relu                                   32                 96                  3                 0          3.11011e-05         20                                              │
│ trial_2e133    RUNNING              2   adam            tanh                                   16                 96                  3                 1          0.00329571          17                                              │
│ trial_2e133    RUNNING              3   rmsprop         relu                                   32                 64                  5                 1          0.00302727          18                                              │
│ trial_2e133    RUNNING              2   adam            relu                                   16                 32                  5                 0          8.69755e-05         18                                              │
│ trial_2e133    RUNNING              3   adam            relu                                   16                 32                  5                 0          0.000149645         28                                              │
│ trial_2e133    RUNNING              2   rmsprop         relu                                   16                 32                  5                 1          0.00234329          17                                              │
│ trial_2e133    RUNNING              2   rmsprop         tanh                                   16                 96                  5                 1          0.00031027          18                                              │
│ trial_2e133    RUNNING              2   adam            tanh                                   16                 64                  3                 0          0.000308353         26                                              │
│ trial_2e133    RUNNING              2   rmsprop         tanh                                   16                 32                  5                 0          0.000595899         20                                              │
│ trial_2e133    RUNNING              2   adam            tanh                                   32                 32                  3                 0          0.000147791         24                                              │
│ trial_2e133    RUNNING              3   rmsprop         tanh                                   32                 64                  5                 0          2.69047e-05         23                                              │
│ trial_2e133    RUNNING              2   adam            relu                                   16                 32                  5                 0          0.000225987         26                                              │
│ trial_2e133    RUNNING              3   rmsprop         tanh                                   16                 64                  5                 0          4.29062e-05         17                                              │
│ trial_2e133    RUNNING              3   rmsprop         tanh                                   32                 64                  5                 0          0.00161065          19                                              │
│ trial_2e133    RUNNING              3   rmsprop         relu                                   16                 64                  3                 1          0.000108525         28                                              │
│ trial_2e133    RUNNING              2   rmsprop         relu                                   32                 96                  3                 1          1.93582e-05         23                                              │
│ trial_2e133    TERMINATED           2   adam            tanh                                   32                 96                  3                 0          0.000165898         20        1            214.247         0.6875   │
│ trial_2e133    TERMINATED           3   rmsprop         relu                                   32                 32                  5                 0          0.00111465          17        1            221.951         0.713834 │
╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=1160657)[0m 
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[36m(train_cnn_ray_tune pid=1160657)[0m Epoch 7/25[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1160670)[0m 
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[36m(train_cnn_ray_tune pid=1160670)[0m 
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[36m(train_cnn_ray_tune pid=1160657)[0m 
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[36m(train_cnn_ray_tune pid=1160680)[0m 
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[36m(train_cnn_ray_tune pid=1160674)[0m 
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[36m(train_cnn_ray_tune pid=1160680)[0m 
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[36m(train_cnn_ray_tune pid=1160680)[0m 
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[36m(train_cnn_ray_tune pid=1160679)[0m 
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[36m(train_cnn_ray_tune pid=1160676)[0m 
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[36m(train_cnn_ray_tune pid=1160670)[0m 
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[36m(train_cnn_ray_tune pid=1160670)[0m Epoch 9/18[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1160657)[0m 
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[36m(train_cnn_ray_tune pid=1160675)[0m 
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[36m(train_cnn_ray_tune pid=1160680)[0m 
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[36m(train_cnn_ray_tune pid=1160662)[0m 
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[36m(train_cnn_ray_tune pid=1160662)[0m 
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[36m(train_cnn_ray_tune pid=1160662)[0m 
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[36m(train_cnn_ray_tune pid=1160662)[0m 
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[36m(train_cnn_ray_tune pid=1160662)[0m 
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[36m(train_cnn_ray_tune pid=1160662)[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=1160662)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=1160663)[0m 
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[36m(train_cnn_ray_tune pid=1160662)[0m 
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[36m(train_cnn_ray_tune pid=1160658)[0m 
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[36m(train_cnn_ray_tune pid=1160662)[0m 
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[36m(train_cnn_ray_tune pid=1160662)[0m 
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[36m(train_cnn_ray_tune pid=1160680)[0m 
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[36m(train_cnn_ray_tune pid=1160663)[0m 
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[36m(train_cnn_ray_tune pid=1160662)[0m 
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[36m(train_cnn_ray_tune pid=1160679)[0m 
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[36m(train_cnn_ray_tune pid=1160658)[0m Epoch 9/20
[36m(train_cnn_ray_tune pid=1160676)[0m Epoch 8/26
[36m(train_cnn_ray_tune pid=1160676)[0m 
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[36m(train_cnn_ray_tune pid=1160676)[0m 
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[36m(train_cnn_ray_tune pid=1160657)[0m 
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Trial trial_2e133 finished iteration 1 at 2025-11-08 16:44:10. Total running time: 4min 12s
╭──────────────────────────────────────╮
│ Trial trial_2e133 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             249.036 │
│ time_total_s                 249.036 │
│ training_iteration                 1 │
│ val_accuracy                 0.72191 │
╰──────────────────────────────────────╯

Trial trial_2e133 completed after 1 iterations at 2025-11-08 16:44:10. Total running time: 4min 12s
[36m(train_cnn_ray_tune pid=1160679)[0m 
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[36m(train_cnn_ray_tune pid=1160676)[0m 
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[36m(train_cnn_ray_tune pid=1160680)[0m 
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[36m(train_cnn_ray_tune pid=1160659)[0m 
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[36m(train_cnn_ray_tune pid=1160675)[0m 
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[36m(train_cnn_ray_tune pid=1160679)[0m 
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Trial trial_2e133 finished iteration 1 at 2025-11-08 16:44:12. Total running time: 4min 14s
╭──────────────────────────────────────╮
│ Trial trial_2e133 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             251.589 │
│ time_total_s                 251.589 │
│ training_iteration                 1 │
│ val_accuracy                 0.70049 │
╰──────────────────────────────────────╯

Trial trial_2e133 completed after 1 iterations at 2025-11-08 16:44:12. Total running time: 4min 14s
[36m(train_cnn_ray_tune pid=1160659)[0m 
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[36m(train_cnn_ray_tune pid=1160660)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160663)[0m 
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[36m(train_cnn_ray_tune pid=1160659)[0m 
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[36m(train_cnn_ray_tune pid=1160659)[0m Epoch 11/27[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1160657)[0m 
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[36m(train_cnn_ray_tune pid=1160660)[0m 
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[36m(train_cnn_ray_tune pid=1160672)[0m 
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[36m(train_cnn_ray_tune pid=1160677)[0m 
[1m  1/310[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m43s[0m 141ms/step - accuracy: 0.6875 - loss: 0.6427
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[36m(train_cnn_ray_tune pid=1160679)[0m 
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 21ms/step
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 22ms/step[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=1160671)[0m 
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[36m(train_cnn_ray_tune pid=1160680)[0m 
[1m  1/619[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:03[0m 102ms/step - accuracy: 0.8125 - loss: 1.0259[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160673)[0m 
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[36m(train_cnn_ray_tune pid=1160673)[0m 
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[36m(train_cnn_ray_tune pid=1160681)[0m 
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[36m(train_cnn_ray_tune pid=1160659)[0m 
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[36m(train_cnn_ray_tune pid=1160671)[0m 
[36m(train_cnn_ray_tune pid=1160671)[0m 
[1m448/619[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m10s[0m 59ms/step - accuracy: 0.8594 - loss: 0.3864
[36m(train_cnn_ray_tune pid=1160663)[0m 
[1m619/619[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m45s[0m 73ms/step - accuracy: 0.7343 - loss: 0.6617 - val_accuracy: 0.6661 - val_loss: 0.8506[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=1160663)[0m Epoch 6/28[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=1160663)[0m 
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[36m(train_cnn_ray_tune pid=1160677)[0m 
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[36m(train_cnn_ray_tune pid=1160663)[0m 
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[36m(train_cnn_ray_tune pid=1160663)[0m 
[1m  1/619[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:42[0m 167ms/step - accuracy: 0.5000 - loss: 1.1341[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=1160657)[0m 
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[36m(train_cnn_ray_tune pid=1160658)[0m 
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[36m(train_cnn_ray_tune pid=1160658)[0m 
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[1m351/619[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m9s[0m 37ms/step - accuracy: 0.7791 - loss: 0.5371
[36m(train_cnn_ray_tune pid=1160675)[0m 
[1m619/619[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m38s[0m 61ms/step - accuracy: 0.7310 - loss: 0.6734 - val_accuracy: 0.6422 - val_loss: 0.9068
[36m(train_cnn_ray_tune pid=1160675)[0m Epoch 7/28
[36m(train_cnn_ray_tune pid=1160674)[0m 
[1m310/310[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m25s[0m 81ms/step - accuracy: 0.7593 - loss: 0.5779 - val_accuracy: 0.6998 - val_loss: 0.7700
[36m(train_cnn_ray_tune pid=1160674)[0m Epoch 10/19
[36m(train_cnn_ray_tune pid=1160675)[0m 
[1m  3/619[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m25s[0m 41ms/step - accuracy: 0.7292 - loss: 0.5831  
[1m  5/619[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m23s[0m 38ms/step - accuracy: 0.7325 - loss: 0.5933
[36m(train_cnn_ray_tune pid=1160674)[0m 
[1m  2/310[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m25s[0m 84ms/step - accuracy: 0.8359 - loss: 0.4568 
[1m  3/310[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m23s[0m 77ms/step - accuracy: 0.8316 - loss: 0.4516
[36m(train_cnn_ray_tune pid=1160670)[0m 
[1m  1/310[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m44s[0m 145ms/step - accuracy: 0.9375 - loss: 0.3076
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[36m(train_cnn_ray_tune pid=1160675)[0m 
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[1m 17/619[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m27s[0m 45ms/step - accuracy: 0.7169 - loss: 0.6821[32m [repeated 148x across cluster][0m
[36m(train_cnn_ray_tune pid=1160681)[0m 
[1m102/310[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m13s[0m 66ms/step - accuracy: 0.8095 - loss: 0.5385[32m [repeated 221x across cluster][0m
[36m(train_cnn_ray_tune pid=1160673)[0m 
[1m  2/619[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m39s[0m 64ms/step - accuracy: 0.7188 - loss: 0.5715  
[36m(train_cnn_ray_tune pid=1160673)[0m 
[1m  1/619[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:26[0m 140ms/step - accuracy: 0.7500 - loss: 0.4724[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=1160657)[0m 
[1m615/619[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 48ms/step - accuracy: 0.7556 - loss: 0.6322[32m [repeated 200x across cluster][0m
[36m(train_cnn_ray_tune pid=1160680)[0m 
[1m348/619[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m9s[0m 35ms/step - accuracy: 0.8296 - loss: 0.4861
[1m349/619[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m9s[0m 35ms/step - accuracy: 0.8296 - loss: 0.4861[32m [repeated 92x across cluster][0m

Trial status: 16 RUNNING | 4 TERMINATED
Current time: 2025-11-08 16:44:28. Total running time: 4min 30s
Logical resource usage: 16.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     num_resblocks     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_2e133    RUNNING              2   rmsprop         relu                                   16                 96                  3                 1          6.07697e-05         25                                              │
│ trial_2e133    RUNNING              3   adam            relu                                   32                 32                  5                 1          1.30564e-05         27                                              │
│ trial_2e133    RUNNING              3   adam            relu                                   32                 96                  3                 0          3.11011e-05         20                                              │
│ trial_2e133    RUNNING              2   adam            tanh                                   16                 96                  3                 1          0.00329571          17                                              │
│ trial_2e133    RUNNING              3   rmsprop         relu                                   32                 64                  5                 1          0.00302727          18                                              │
│ trial_2e133    RUNNING              2   adam            relu                                   16                 32                  5                 0          8.69755e-05         18                                              │
│ trial_2e133    RUNNING              3   adam            relu                                   16                 32                  5                 0          0.000149645         28                                              │
│ trial_2e133    RUNNING              2   rmsprop         tanh                                   16                 96                  5                 1          0.00031027          18                                              │
│ trial_2e133    RUNNING              2   adam            tanh                                   16                 64                  3                 0          0.000308353         26                                              │
│ trial_2e133    RUNNING              2   rmsprop         tanh                                   16                 32                  5                 0          0.000595899         20                                              │
│ trial_2e133    RUNNING              3   rmsprop         tanh                                   32                 64                  5                 0          2.69047e-05         23                                              │
│ trial_2e133    RUNNING              2   adam            relu                                   16                 32                  5                 0          0.000225987         26                                              │
│ trial_2e133    RUNNING              3   rmsprop         tanh                                   16                 64                  5                 0          4.29062e-05         17                                              │
│ trial_2e133    RUNNING              3   rmsprop         tanh                                   32                 64                  5                 0          0.00161065          19                                              │
│ trial_2e133    RUNNING              3   rmsprop         relu                                   16                 64                  3                 1          0.000108525         28                                              │
│ trial_2e133    RUNNING              2   rmsprop         relu                                   32                 96                  3                 1          1.93582e-05         23                                              │
│ trial_2e133    TERMINATED           2   adam            tanh                                   32                 96                  3                 0          0.000165898         20        1            214.247         0.6875   │
│ trial_2e133    TERMINATED           2   rmsprop         relu                                   16                 32                  5                 1          0.00234329          17        1            249.036         0.72191  │
│ trial_2e133    TERMINATED           3   rmsprop         relu                                   32                 32                  5                 0          0.00111465          17        1            221.951         0.713834 │
│ trial_2e133    TERMINATED           2   adam            tanh                                   32                 32                  3                 0          0.000147791         24        1            251.589         0.700492 │
╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=1160676)[0m 
[1m470/619[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m6s[0m 41ms/step - accuracy: 0.8199 - loss: 0.4230
[1m472/619[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m6s[0m 41ms/step - accura
[36m(train_cnn_ray_tune pid=1160676)[0m cy: 0.8199 - loss: 0.4230
[36m(train_cnn_ray_tune pid=1160673)[0m 
[1m619/619[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m47s[0m 75ms/step - accuracy: 0.7516 - loss: 0.6431 - val_accuracy: 0.6369 - val_loss: 0.7878[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1160673)[0m Epoch 6/17[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1160663)[0m 
[1m178/619[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m25s[0m 59ms/step - accuracy: 0.7506 - loss: 0.6005
[1m179/619[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m25s[0m 59ms/step - accuracy: 0.7507 - loss: 0.6005
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[36m(train_cnn_ray_tune pid=1160674)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160671)[0m 
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[36m(train_cnn_ray_tune pid=1160657)[0m 
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[36m(train_cnn_ray_tune pid=1160672)[0m 
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[36m(train_cnn_ray_tune pid=1160680)[0m 
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[36m(train_cnn_ray_tune pid=1160676)[0m 
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[36m(train_cnn_ray_tune pid=1160677)[0m 
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[36m(train_cnn_ray_tune pid=1160658)[0m 
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[36m(train_cnn_ray_tune pid=1160658)[0m Epoch 10/20[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=1160675)[0m 
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[36m(train_cnn_ray_tune pid=1160657)[0m 
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[36m(train_cnn_ray_tune pid=1160658)[0m 
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[36m(train_cnn_ray_tune pid=1160670)[0m 
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[36m(train_cnn_ray_tune pid=1160660)[0m 
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[36m(train_cnn_ray_tune pid=1160681)[0m 
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[36m(train_cnn_ray_tune pid=1160658)[0m 
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[36m(train_cnn_ray_tune pid=1160672)[0m 
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[36m(train_cnn_ray_tune pid=1160676)[0m 
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[36m(train_cnn_ray_tune pid=1160675)[0m 
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[36m(train_cnn_ray_tune pid=1160657)[0m 
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[36m(train_cnn_ray_tune pid=1160663)[0m 
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[36m(train_cnn_ray_tune pid=1160676)[0m 
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[36m(train_cnn_ray_tune pid=1160676)[0m 
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[36m(train_cnn_ray_tune pid=1160676)[0m 
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[36m(train_cnn_ray_tune pid=1160676)[0m 
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[36m(train_cnn_ray_tune pid=1160676)[0m 
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[36m(train_cnn_ray_tune pid=1160676)[0m 
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[36m(train_cnn_ray_tune pid=1160676)[0m 
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[36m(train_cnn_ray_tune pid=1160676)[0m 
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[36m(train_cnn_ray_tune pid=1160676)[0m 
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[36m(train_cnn_ray_tune pid=1160676)[0m 
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[36m(train_cnn_ray_tune pid=1160676)[0m 
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[36m(train_cnn_ray_tune pid=1160676)[0m 
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[36m(train_cnn_ray_tune pid=1160676)[0m 
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[36m(train_cnn_ray_tune pid=1160676)[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=1160676)[0m   _log_deprecation_warning([32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1160676)[0m 
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[36m(train_cnn_ray_tune pid=1160676)[0m 
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[36m(train_cnn_ray_tune pid=1160676)[0m 
[1m27/89[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m1s[0m 19ms/step
[1m30/89[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m1s[0m 19ms/step
[36m(train_cnn_ray_tune pid=1160680)[0m 
[1m619/619[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m26s[0m 43ms/step - accuracy: 0.8307 - loss: 0.4786 - val_accuracy: 0.6850 - val_loss: 0.8696
[36m(train_cnn_ray_tune pid=1160680)[0m Epoch 10/26
[36m(train_cnn_ray_tune pid=1160676)[0m 
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[36m(train_cnn_ray_tune pid=1160676)[0m 
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[36m(train_cnn_ray_tune pid=1160676)[0m 
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[36m(train_cnn_ray_tune pid=1160676)[0m 
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[36m(train_cnn_ray_tune pid=1160676)[0m 
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[36m(train_cnn_ray_tune pid=1160676)[0m 
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[36m(train_cnn_ray_tune pid=1160676)[0m 
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[36m(train_cnn_ray_tune pid=1160672)[0m 
[1m  1/619[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:13[0m 119ms/step - accuracy: 0.8125 - loss: 0.5114[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1160672)[0m 
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[36m(train_cnn_ray_tune pid=1160676)[0m 
[1m73/89[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 19ms/step
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[36m(train_cnn_ray_tune pid=1160676)[0m 
[1m79/89[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 19ms/step
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[36m(train_cnn_ray_tune pid=1160677)[0m 
[1m187/310[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m6s[0m 51ms/step - accuracy: 0.7147 - loss: 0.7141[32m [repeated 153x across cluster][0m
[36m(train_cnn_ray_tune pid=1160674)[0m 
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[36m(train_cnn_ray_tune pid=1160676)[0m 
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Trial trial_2e133 finished iteration 1 at 2025-11-08 16:44:43. Total running time: 4min 45s
╭──────────────────────────────────────╮
│ Trial trial_2e133 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             282.792 │
│ time_total_s                 282.792 │
│ training_iteration                 1 │
│ val_accuracy                 0.71735 │
╰──────────────────────────────────────╯

Trial trial_2e133 completed after 1 iterations at 2025-11-08 16:44:43. Total running time: 4min 45s
[36m(train_cnn_ray_tune pid=1160680)[0m 
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[36m(train_cnn_ray_tune pid=1160675)[0m 
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[36m(train_cnn_ray_tune pid=1160660)[0m 
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[36m(train_cnn_ray_tune pid=1160660)[0m Epoch 8/17[32m [repeated 3x across cluster][0m
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[36m(train_cnn_ray_tune pid=1160660)[0m 
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[36m(train_cnn_ray_tune pid=1160670)[0m 
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[36m(train_cnn_ray_tune pid=1160663)[0m 
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[36m(train_cnn_ray_tune pid=1160675)[0m 
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[36m(train_cnn_ray_tune pid=1160674)[0m 
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[36m(train_cnn_ray_tune pid=1160680)[0m 
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[36m(train_cnn_ray_tune pid=1160670)[0m 
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[36m(train_cnn_ray_tune pid=1160658)[0m 
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[36m(train_cnn_ray_tune pid=1160680)[0m 
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[36m(train_cnn_ray_tune pid=1160659)[0m 
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[36m(train_cnn_ray_tune pid=1160659)[0m Epoch 13/27[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=1160680)[0m 
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[36m(train_cnn_ray_tune pid=1160659)[0m 
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[36m(train_cnn_ray_tune pid=1160675)[0m 
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[36m(train_cnn_ray_tune pid=1160657)[0m 
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[36m(train_cnn_ray_tune pid=1160677)[0m 
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[36m(train_cnn_ray_tune pid=1160660)[0m 
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[36m(train_cnn_ray_tune pid=1160670)[0m 
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[36m(train_cnn_ray_tune pid=1160677)[0m 
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[36m(train_cnn_ray_tune pid=1160659)[0m 
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[36m(train_cnn_ray_tune pid=1160674)[0m 
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[36m(train_cnn_ray_tune pid=1160670)[0m 
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[36m(train_cnn_ray_tune pid=1160657)[0m 
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[36m(train_cnn_ray_tune pid=1160673)[0m 
[1m553/619[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m3s[0m 58ms/step - accuracy: 0.7813 - loss: 0.5888
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Trial status: 15 RUNNING | 5 TERMINATED
Current time: 2025-11-08 16:44:58. Total running time: 5min 0s
Logical resource usage: 15.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     num_resblocks     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_2e133    RUNNING              2   rmsprop         relu                                   16                 96                  3                 1          6.07697e-05         25                                              │
│ trial_2e133    RUNNING              3   adam            relu                                   32                 32                  5                 1          1.30564e-05         27                                              │
│ trial_2e133    RUNNING              3   adam            relu                                   32                 96                  3                 0          3.11011e-05         20                                              │
│ trial_2e133    RUNNING              2   adam            tanh                                   16                 96                  3                 1          0.00329571          17                                              │
│ trial_2e133    RUNNING              3   rmsprop         relu                                   32                 64                  5                 1          0.00302727          18                                              │
│ trial_2e133    RUNNING              2   adam            relu                                   16                 32                  5                 0          8.69755e-05         18                                              │
│ trial_2e133    RUNNING              3   adam            relu                                   16                 32                  5                 0          0.000149645         28                                              │
│ trial_2e133    RUNNING              2   rmsprop         tanh                                   16                 96                  5                 1          0.00031027          18                                              │
│ trial_2e133    RUNNING              2   rmsprop         tanh                                   16                 32                  5                 0          0.000595899         20                                              │
│ trial_2e133    RUNNING              3   rmsprop         tanh                                   32                 64                  5                 0          2.69047e-05         23                                              │
│ trial_2e133    RUNNING              2   adam            relu                                   16                 32                  5                 0          0.000225987         26                                              │
│ trial_2e133    RUNNING              3   rmsprop         tanh                                   16                 64                  5                 0          4.29062e-05         17                                              │
│ trial_2e133    RUNNING              3   rmsprop         tanh                                   32                 64                  5                 0          0.00161065          19                                              │
│ trial_2e133    RUNNING              3   rmsprop         relu                                   16                 64                  3                 1          0.000108525         28                                              │
│ trial_2e133    RUNNING              2   rmsprop         relu                                   32                 96                  3                 1          1.93582e-05         23                                              │
│ trial_2e133    TERMINATED           2   adam            tanh                                   32                 96                  3                 0          0.000165898         20        1            214.247         0.6875   │
│ trial_2e133    TERMINATED           2   rmsprop         relu                                   16                 32                  5                 1          0.00234329          17        1            249.036         0.72191  │
│ trial_2e133    TERMINATED           2   adam            tanh                                   16                 64                  3                 0          0.000308353         26        1            282.792         0.717346 │
│ trial_2e133    TERMINATED           3   rmsprop         relu                                   32                 32                  5                 0          0.00111465          17        1            221.951         0.713834 │
│ trial_2e133    TERMINATED           2   adam            tanh                                   32                 32                  3                 0          0.000147791         24        1            251.589         0.700492 │
╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=1160658)[0m 
[1m619/619[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m25s[0m 40ms/step - accuracy: 0.7934 - loss: 0.5182 - val_accuracy: 0.6629 - val_loss: 0.7899
[36m(train_cnn_ray_tune pid=1160658)[0m Epoch 11/20
[36m(train_cnn_ray_tune pid=1160658)[0m 
[1m  1/619[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:12[0m 118ms/step - accuracy: 0.8125 - loss: 0.6410
[1m  3/619[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m21s[0m 34ms/step - accuracy: 0.8194 - loss: 0.5659  
[36m(train_cnn_ray_tune pid=1160675)[0m 
[1m  1/619[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:33[0m 151ms/step - accuracy: 0.7500 - loss: 0.4843
[36m(train_cnn_ray_tune pid=1160675)[0m 
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[36m(train_cnn_ray_tune pid=1160681)[0m 
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[36m(train_cnn_ray_tune pid=1160681)[0m 
[1m  3/310[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m32s[0m 105ms/step - accuracy: 0.7899 - loss: 0.6310
[36m(train_cnn_ray_tune pid=1160681)[0m 
[1m  4/310[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m27s[0m 91ms/step - accuracy: 0.7975 - loss: 0.6058 
[36m(train_cnn_ray_tune pid=1160663)[0m 
[1m 12/619[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m39s[0m 64ms/step - accuracy: 0.7328 - loss: 0.6840
[1m 13/619[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m38s[0m 64ms/step - accuracy: 0.7345 - loss: 0.6809[32m [repeated 89x across cluster][0m
[36m(train_cnn_ray_tune pid=1160661)[0m 
[1m164/310[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m10s[0m 73ms/step - accuracy: 0.7704 - loss: 0.5988[32m [repeated 107x across cluster][0m
[36m(train_cnn_ray_tune pid=1160680)[0m 
[1m504/619[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m3s[0m 32ms/step - accuracy: 0.8508 - loss: 0.4249
[1m506/619[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m3s[0m 32ms/step - accuracy: 0.8508 - loss: 0.4249
[1m508/619[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m3s[0m 32ms/step - accuracy: 0.8508 - loss: 0.4250[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1160675)[0m 
[1m 30/619[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m32s[0m 55ms/step - accuracy: 0.7930 - loss: 0.5177
[1m 31/619[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m32s[0m 55ms/step - accuracy: 0.7930 - loss: 0.5183
[1m 32/619[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m32s[0m 55ms/step - accuracy: 0.7929 - loss: 0.5190
[36m(train_cnn_ray_tune pid=1160674)[0m 
[1m150/310[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m9s[0m 62ms/step - accuracy: 0.7832 - loss: 0.5362 
[1m151/310[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m9s[0m 62ms/step - accuracy: 0.7832 - loss: 0.5362[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1160660)[0m 
[1m453/619[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m7s[0m 43ms/step - accuracy: 0.7187 - loss: 0.6243[32m [repeated 214x across cluster][0m
[36m(train_cnn_ray_tune pid=1160659)[0m 
[1m234/310[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m4s[0m 54ms/step - accuracy: 0.6142 - loss: 0.9316
[1m235/310[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m4s[0m 54ms/step - accuracy: 0.6142 - loss: 0.9316[32m [repeated 198x across cluster][0m
[36m(train_cnn_ray_tune pid=1160663)[0m 
[1m619/619[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m41s[0m 65ms/step - accuracy: 0.7568 - loss: 0.6024 - val_accuracy: 0.6822 - val_loss: 0.8722[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1160663)[0m Epoch 7/28[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1160663)[0m 
[1m  1/619[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:32[0m 150ms/step - accuracy: 0.7500 - loss: 0.5982
[1m  2/619[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m56s[0m 91ms/step - accuracy: 0.7500 - loss: 0.6353  
[36m(train_cnn_ray_tune pid=1160657)[0m 
[1m  1/619[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:19[0m 129ms/step - accuracy: 0.9375 - loss: 0.3998[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1160657)[0m 
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[36m(train_cnn_ray_tune pid=1160681)[0m 
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[36m(train_cnn_ray_tune pid=1160680)[0m 
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[36m(train_cnn_ray_tune pid=1160680)[0m 
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[36m(train_cnn_ray_tune pid=1160660)[0m 
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[36m(train_cnn_ray_tune pid=1160680)[0m 
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[36m(train_cnn_ray_tune pid=1160673)[0m 
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[36m(train_cnn_ray_tune pid=1160673)[0m Epoch 7/17[32m [repeated 5x across cluster][0m
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[36m(train_cnn_ray_tune pid=1160681)[0m 
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[36m(train_cnn_ray_tune pid=1160680)[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=1160680)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=1160680)[0m 
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[36m(train_cnn_ray_tune pid=1160672)[0m 
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[36m(train_cnn_ray_tune pid=1160680)[0m 
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[36m(train_cnn_ray_tune pid=1160681)[0m 
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[36m(train_cnn_ray_tune pid=1160663)[0m 
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[36m(train_cnn_ray_tune pid=1160657)[0m 
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Trial trial_2e133 finished iteration 1 at 2025-11-08 16:45:10. Total running time: 5min 12s
╭──────────────────────────────────────╮
│ Trial trial_2e133 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             309.671 │
│ time_total_s                 309.671 │
│ training_iteration                 1 │
│ val_accuracy                 0.68855 │
╰──────────────────────────────────────╯

Trial trial_2e133 completed after 1 iterations at 2025-11-08 16:45:10. Total running time: 5min 12s
[36m(train_cnn_ray_tune pid=1160663)[0m 
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[36m(train_cnn_ray_tune pid=1160670)[0m 
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[36m(train_cnn_ray_tune pid=1160670)[0m 
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[36m(train_cnn_ray_tune pid=1160659)[0m 
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[36m(train_cnn_ray_tune pid=1160674)[0m 
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[36m(train_cnn_ray_tune pid=1160681)[0m 
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[36m(train_cnn_ray_tune pid=1160677)[0m 
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[36m(train_cnn_ray_tune pid=1160670)[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=1160670)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=1160681)[0m 
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[36m(train_cnn_ray_tune pid=1160674)[0m 
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[36m(train_cnn_ray_tune pid=1160674)[0m Epoch 12/19[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160660)[0m 
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[36m(train_cnn_ray_tune pid=1160658)[0m 
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[36m(train_cnn_ray_tune pid=1160663)[0m 
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[36m(train_cnn_ray_tune pid=1160657)[0m 
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Trial trial_2e133 finished iteration 1 at 2025-11-08 16:45:15. Total running time: 5min 18s
╭──────────────────────────────────────╮
│ Trial trial_2e133 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             314.836 │
│ time_total_s                 314.836 │
│ training_iteration                 1 │
│ val_accuracy                 0.71559 │
╰──────────────────────────────────────╯

Trial trial_2e133 completed after 1 iterations at 2025-11-08 16:45:15. Total running time: 5min 18s
[36m(train_cnn_ray_tune pid=1160681)[0m 
[36m(train_cnn_ray_tune pid=1160681)[0m 
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[36m(train_cnn_ray_tune pid=1160663)[0m 
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[36m(train_cnn_ray_tune pid=1160659)[0m 
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[36m(train_cnn_ray_tune pid=1160658)[0m 
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[36m(train_cnn_ray_tune pid=1160659)[0m 
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[36m(train_cnn_ray_tune pid=1160681)[0m 
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[36m(train_cnn_ray_tune pid=1160660)[0m 
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[36m(train_cnn_ray_tune pid=1160660)[0m Epoch 9/17[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1160663)[0m 
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[36m(train_cnn_ray_tune pid=1160673)[0m 
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[36m(train_cnn_ray_tune pid=1160660)[0m 
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[36m(train_cnn_ray_tune pid=1160674)[0m 
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[36m(train_cnn_ray_tune pid=1160681)[0m 
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[36m(train_cnn_ray_tune pid=1160663)[0m 
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[36m(train_cnn_ray_tune pid=1160674)[0m 
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[36m(train_cnn_ray_tune pid=1160677)[0m 
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[36m(train_cnn_ray_tune pid=1160659)[0m 
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[36m(train_cnn_ray_tune pid=1160663)[0m 
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[36m(train_cnn_ray_tune pid=1160677)[0m 
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[36m(train_cnn_ray_tune pid=1160677)[0m Epoch 17/23[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1160658)[0m 
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[36m(train_cnn_ray_tune pid=1160658)[0m 
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[36m(train_cnn_ray_tune pid=1160658)[0m 
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[36m(train_cnn_ray_tune pid=1160681)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160658)[0m 
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[36m(train_cnn_ray_tune pid=1160663)[0m 
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[36m(train_cnn_ray_tune pid=1160677)[0m 
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[36m(train_cnn_ray_tune pid=1160663)[0m 
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[36m(train_cnn_ray_tune pid=1160673)[0m 
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[36m(train_cnn_ray_tune pid=1160658)[0m 
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[36m(train_cnn_ray_tune pid=1160658)[0m 
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[36m(train_cnn_ray_tune pid=1160658)[0m 
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[36m(train_cnn_ray_tune pid=1160658)[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=1160658)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=1160658)[0m 
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[36m(train_cnn_ray_tune pid=1160658)[0m 
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[36m(train_cnn_ray_tune pid=1160658)[0m 
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[36m(train_cnn_ray_tune pid=1160672)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160658)[0m 
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Trial status: 13 RUNNING | 7 TERMINATED
Current time: 2025-11-08 16:45:28. Total running time: 5min 30s
Logical resource usage: 13.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     num_resblocks     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_2e133    RUNNING              2   rmsprop         relu                                   16                 96                  3                 1          6.07697e-05         25                                              │
│ trial_2e133    RUNNING              3   adam            relu                                   32                 32                  5                 1          1.30564e-05         27                                              │
│ trial_2e133    RUNNING              3   adam            relu                                   32                 96                  3                 0          3.11011e-05         20                                              │
│ trial_2e133    RUNNING              2   adam            tanh                                   16                 96                  3                 1          0.00329571          17                                              │
│ trial_2e133    RUNNING              2   adam            relu                                   16                 32                  5                 0          8.69755e-05         18                                              │
│ trial_2e133    RUNNING              3   adam            relu                                   16                 32                  5                 0          0.000149645         28                                              │
│ trial_2e133    RUNNING              2   rmsprop         tanh                                   16                 96                  5                 1          0.00031027          18                                              │
│ trial_2e133    RUNNING              2   rmsprop         tanh                                   16                 32                  5                 0          0.000595899         20                                              │
│ trial_2e133    RUNNING              3   rmsprop         tanh                                   32                 64                  5                 0          2.69047e-05         23                                              │
│ trial_2e133    RUNNING              3   rmsprop         tanh                                   16                 64                  5                 0          4.29062e-05         17                                              │
│ trial_2e133    RUNNING              3   rmsprop         tanh                                   32                 64                  5                 0          0.00161065          19                                              │
│ trial_2e133    RUNNING              3   rmsprop         relu                                   16                 64                  3                 1          0.000108525         28                                              │
│ trial_2e133    RUNNING              2   rmsprop         relu                                   32                 96                  3                 1          1.93582e-05         23                                              │
│ trial_2e133    TERMINATED           2   adam            tanh                                   32                 96                  3                 0          0.000165898         20        1            214.247         0.6875   │
│ trial_2e133    TERMINATED           3   rmsprop         relu                                   32                 64                  5                 1          0.00302727          18        1            314.836         0.71559  │
│ trial_2e133    TERMINATED           2   rmsprop         relu                                   16                 32                  5                 1          0.00234329          17        1            249.036         0.72191  │
│ trial_2e133    TERMINATED           2   adam            tanh                                   16                 64                  3                 0          0.000308353         26        1            282.792         0.717346 │
│ trial_2e133    TERMINATED           3   rmsprop         relu                                   32                 32                  5                 0          0.00111465          17        1            221.951         0.713834 │
│ trial_2e133    TERMINATED           2   adam            tanh                                   32                 32                  3                 0          0.000147791         24        1            251.589         0.700492 │
│ trial_2e133    TERMINATED           2   adam            relu                                   16                 32                  5                 0          0.000225987         26        1            309.671         0.688553 │
╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

Trial trial_2e133 finished iteration 1 at 2025-11-08 16:45:28. Total running time: 5min 30s
╭──────────────────────────────────────╮
│ Trial trial_2e133 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             327.623 │
│ time_total_s                 327.623 │
│ training_iteration                 1 │
│ val_accuracy                 0.69382 │
╰──────────────────────────────────────╯

Trial trial_2e133 completed after 1 iterations at 2025-11-08 16:45:28. Total running time: 5min 30s
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[36m(train_cnn_ray_tune pid=1160674)[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=1160674)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=1160657)[0m 
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[36m(train_cnn_ray_tune pid=1160674)[0m 
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[36m(train_cnn_ray_tune pid=1160674)[0m 
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[36m(train_cnn_ray_tune pid=1160674)[0m 
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[36m(train_cnn_ray_tune pid=1160674)[0m 
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[36m(train_cnn_ray_tune pid=1160674)[0m 
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[36m(train_cnn_ray_tune pid=1160674)[0m 
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[36m(train_cnn_ray_tune pid=1160674)[0m 
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[36m(train_cnn_ray_tune pid=1160657)[0m 
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[36m(train_cnn_ray_tune pid=1160672)[0m 
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[36m(train_cnn_ray_tune pid=1160674)[0m 
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[36m(train_cnn_ray_tune pid=1160674)[0m 
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[36m(train_cnn_ray_tune pid=1160674)[0m 
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[36m(train_cnn_ray_tune pid=1160674)[0m 
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Trial trial_2e133 finished iteration 1 at 2025-11-08 16:45:36. Total running time: 5min 38s
╭──────────────────────────────────────╮
│ Trial trial_2e133 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             335.871 │
│ time_total_s                 335.871 │
│ training_iteration                 1 │
│ val_accuracy                 0.70365 │
╰──────────────────────────────────────╯

Trial trial_2e133 completed after 1 iterations at 2025-11-08 16:45:36. Total running time: 5min 38s
[36m(train_cnn_ray_tune pid=1160674)[0m 
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[36m(train_cnn_ray_tune pid=1160677)[0m Epoch 18/23[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=1160657)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160671)[0m 
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[36m(train_cnn_ray_tune pid=1160671)[0m 
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[36m(train_cnn_ray_tune pid=1160673)[0m 
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[36m(train_cnn_ray_tune pid=1160659)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160675)[0m 
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[36m(train_cnn_ray_tune pid=1160675)[0m 
[1m227/619[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m13s[0m 34ms/step - accuracy: 0.7974 - loss: 0.5234
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Trial trial_2e133 finished iteration 1 at 2025-11-08 16:45:40. Total running time: 5min 42s
╭──────────────────────────────────────╮
│ Trial trial_2e133 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s              339.39 │
│ time_total_s                  339.39 │
│ training_iteration                 1 │
│ val_accuracy                 0.67205 │
╰──────────────────────────────────────╯

Trial trial_2e133 completed after 1 iterations at 2025-11-08 16:45:40. Total running time: 5min 42s
[36m(train_cnn_ray_tune pid=1160681)[0m 
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[36m(train_cnn_ray_tune pid=1160681)[0m 
[1m 4/43[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 17ms/step  
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[36m(train_cnn_ray_tune pid=1160663)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160660)[0m Epoch 10/17[32m [repeated 2x across cluster][0m
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Trial trial_2e133 finished iteration 1 at 2025-11-08 16:45:43. Total running time: 5min 46s
[36m(train_cnn_ray_tune pid=1160681)[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=1160681)[0m   _log_deprecation_warning([32m [repeated 2x across cluster][0m
╭──────────────────────────────────────╮
│ Trial trial_2e133 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             342.942 │
│ time_total_s                 342.942 │
│ training_iteration                 1 │
│ val_accuracy                 0.61833 │
╰──────────────────────────────────────╯

Trial trial_2e133 completed after 1 iterations at 2025-11-08 16:45:43. Total running time: 5min 46s
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[36m(train_cnn_ray_tune pid=1160659)[0m Epoch 16/27[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1160677)[0m 
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[36m(train_cnn_ray_tune pid=1160657)[0m 
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[36m(train_cnn_ray_tune pid=1160672)[0m 
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[36m(train_cnn_ray_tune pid=1160673)[0m 
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[36m(train_cnn_ray_tune pid=1160657)[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=1160657)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=1160675)[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=1160675)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=1160673)[0m 
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[36m(train_cnn_ray_tune pid=1160672)[0m Epoch 13/18[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160673)[0m 
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[36m(train_cnn_ray_tune pid=1160657)[0m 
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[36m(train_cnn_ray_tune pid=1160660)[0m 
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[36m(train_cnn_ray_tune pid=1160663)[0m 
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[36m(train_cnn_ray_tune pid=1160657)[0m 
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[36m(train_cnn_ray_tune pid=1160659)[0m 
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[36m(train_cnn_ray_tune pid=1160659)[0m 
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[36m(train_cnn_ray_tune pid=1160675)[0m 
[1m 1/43[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m26s[0m 623ms/step
[36m(train_cnn_ray_tune pid=1160675)[0m 
[1m 6/43[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 14ms/step  
[1m10/43[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 14ms/step

Trial trial_2e133 finished iteration 1 at 2025-11-08 16:45:55. Total running time: 5min 57s
╭──────────────────────────────────────╮
│ Trial trial_2e133 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             354.604 │
│ time_total_s                 354.604 │
│ training_iteration                 1 │
│ val_accuracy                  0.6861 │
╰──────────────────────────────────────╯

Trial trial_2e133 completed after 1 iterations at 2025-11-08 16:45:55. Total running time: 5min 57s
[36m(train_cnn_ray_tune pid=1160675)[0m 
[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 54ms/step
[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160677)[0m Epoch 20/23[32m [repeated 3x across cluster][0m

Trial trial_2e133 finished iteration 1 at 2025-11-08 16:45:56. Total running time: 5min 59s
╭──────────────────────────────────────╮
│ Trial trial_2e133 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             356.311 │
│ time_total_s                 356.311 │
│ training_iteration                 1 │
│ val_accuracy                 0.66959 │
╰──────────────────────────────────────╯

Trial trial_2e133 completed after 1 iterations at 2025-11-08 16:45:56. Total running time: 5min 59s
[36m(train_cnn_ray_tune pid=1160659)[0m 
[1m  3/310[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 31ms/step - accuracy: 0.5747 - loss: 0.9750 
[1m  5/310[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 31ms/step - accuracy: 0.5726 - loss: 0.9753
[36m(train_cnn_ray_tune pid=1160675)[0m 
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 12ms/step[32m [repeated 33x across cluster][0m

Trial status: 13 TERMINATED | 7 RUNNING
Current time: 2025-11-08 16:45:58. Total running time: 6min 0s
Logical resource usage: 7.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     num_resblocks     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_2e133    RUNNING              3   adam            relu                                   32                 32                  5                 1          1.30564e-05         27                                              │
│ trial_2e133    RUNNING              3   adam            relu                                   32                 96                  3                 0          3.11011e-05         20                                              │
│ trial_2e133    RUNNING              2   adam            tanh                                   16                 96                  3                 1          0.00329571          17                                              │
│ trial_2e133    RUNNING              2   adam            relu                                   16                 32                  5                 0          8.69755e-05         18                                              │
│ trial_2e133    RUNNING              3   rmsprop         tanh                                   16                 64                  5                 0          4.29062e-05         17                                              │
│ trial_2e133    RUNNING              3   rmsprop         relu                                   16                 64                  3                 1          0.000108525         28                                              │
│ trial_2e133    RUNNING              2   rmsprop         relu                                   32                 96                  3                 1          1.93582e-05         23                                              │
│ trial_2e133    TERMINATED           2   rmsprop         relu                                   16                 96                  3                 1          6.07697e-05         25        1            354.604         0.686096 │
│ trial_2e133    TERMINATED           2   adam            tanh                                   32                 96                  3                 0          0.000165898         20        1            214.247         0.6875   │
│ trial_2e133    TERMINATED           3   rmsprop         relu                                   32                 64                  5                 1          0.00302727          18        1            314.836         0.71559  │
│ trial_2e133    TERMINATED           3   adam            relu                                   16                 32                  5                 0          0.000149645         28        1            356.311         0.669593 │
│ trial_2e133    TERMINATED           2   rmsprop         relu                                   16                 32                  5                 1          0.00234329          17        1            249.036         0.72191  │
│ trial_2e133    TERMINATED           2   rmsprop         tanh                                   16                 96                  5                 1          0.00031027          18        1            339.39          0.672051 │
│ trial_2e133    TERMINATED           2   adam            tanh                                   16                 64                  3                 0          0.000308353         26        1            282.792         0.717346 │
│ trial_2e133    TERMINATED           3   rmsprop         relu                                   32                 32                  5                 0          0.00111465          17        1            221.951         0.713834 │
│ trial_2e133    TERMINATED           2   rmsprop         tanh                                   16                 32                  5                 0          0.000595899         20        1            327.623         0.69382  │
│ trial_2e133    TERMINATED           2   adam            tanh                                   32                 32                  3                 0          0.000147791         24        1            251.589         0.700492 │
│ trial_2e133    TERMINATED           3   rmsprop         tanh                                   32                 64                  5                 0          2.69047e-05         23        1            342.942         0.618329 │
│ trial_2e133    TERMINATED           2   adam            relu                                   16                 32                  5                 0          0.000225987         26        1            309.671         0.688553 │
│ trial_2e133    TERMINATED           3   rmsprop         tanh                                   32                 64                  5                 0          0.00161065          19        1            335.871         0.703652 │
╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=1160673)[0m 
[1m543/619[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m2s[0m 31ms/step - accuracy: 0.8314 - loss: 0.4792[32m [repeated 128x across cluster][0m
[36m(train_cnn_ray_tune pid=1160677)[0m 
[1m124/310[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.7836 - loss: 0.5929
[1m127/310[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.7836 - loss: 0.5928[32m [repeated 159x across cluster][0m
[36m(train_cnn_ray_tune pid=1160660)[0m 
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[36m(train_cnn_ray_tune pid=1160660)[0m 
[1m 7/43[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 11ms/step  
[36m(train_cnn_ray_tune pid=1160663)[0m 
[1m  1/619[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m50s[0m 81ms/step - accuracy: 0.8125 - loss: 0.4817[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1160660)[0m 
[1m619/619[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m18s[0m 28ms/step - accuracy: 0.7260 - loss: 0.5923 - val_accuracy: 0.7159 - val_loss: 0.6438[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=1160660)[0m 
[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 62ms/step

Trial trial_2e133 finished iteration 1 at 2025-11-08 16:46:01. Total running time: 6min 3s
╭──────────────────────────────────────╮
│ Trial trial_2e133 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             360.147 │
│ time_total_s                 360.147 │
│ training_iteration                 1 │
│ val_accuracy                 0.71594 │
╰──────────────────────────────────────╯

Trial trial_2e133 completed after 1 iterations at 2025-11-08 16:46:01. Total running time: 6min 3s
[36m(train_cnn_ray_tune pid=1160663)[0m 
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[36m(train_cnn_ray_tune pid=1160663)[0m 
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[36m(train_cnn_ray_tune pid=1160660)[0m 
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[36m(train_cnn_ray_tune pid=1160672)[0m 
[1m  1/619[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m40s[0m 66ms/step - accuracy: 0.8125 - loss: 0.5992
[1m  5/619[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 14ms/step - accuracy: 0.8744 - loss: 0.4550 
[36m(train_cnn_ray_tune pid=1160672)[0m Epoch 14/18[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1160673)[0m 
[1m  5/619[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 16ms/step - accuracy: 0.8329 - loss: 0.4608 
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[36m(train_cnn_ray_tune pid=1160660)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160659)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
[1m  1/310[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m25s[0m 84ms/step - accuracy: 0.8438 - loss: 0.3182[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160673)[0m 
[1m120/619[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m11s[0m 22ms/step - accuracy: 0.8427 - loss: 0.4642
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[36m(train_cnn_ray_tune pid=1160673)[0m 
[1m130/619[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m10s[0m 22ms/step - accuracy: 0.8426 - loss: 0.4638
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[36m(train_cnn_ray_tune pid=1160673)[0m 
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[36m(train_cnn_ray_tune pid=1160659)[0m 
[1m  1/310[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m21s[0m 69ms/step - accuracy: 0.7188 - loss: 0.7702
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[36m(train_cnn_ray_tune pid=1160661)[0m Epoch 13/20[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=1160673)[0m 
[1m192/619[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m9s[0m 23ms/step - accuracy: 0.8430 - loss: 0.4605 
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[36m(train_cnn_ray_tune pid=1160673)[0m 
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[36m(train_cnn_ray_tune pid=1160659)[0m 
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[1m271/310[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 23ms/step - accuracy: 0.6328 - loss: 0.8838[32m [repeated 164x across cluster][0m
[36m(train_cnn_ray_tune pid=1160677)[0m 
[1m310/310[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 23ms/step - accuracy: 0.7801 - loss: 0.5842 - val_accuracy: 0.6591 - val_loss: 0.8251
[36m(train_cnn_ray_tune pid=1160677)[0m 
[1m  1/310[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m25s[0m 82ms/step - accuracy: 0.7812 - loss: 0.5661
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[36m(train_cnn_ray_tune pid=1160673)[0m 
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[1m189/619[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m10s[0m 23ms/step - accuracy: 0.8430 - loss: 0.4606[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=1160673)[0m 
[1m185/619[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m10s[0m 23ms/step - accuracy: 0.8429 - loss: 0.4607[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=1160659)[0m Epoch 19/27[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1160661)[0m 
[1m299/310[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 30ms/step - accuracy: 0.8391 - loss: 0.4504
[1m301/310[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 30ms/step - accuracy: 0.8391 - loss: 0.4503
[1m303/310[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 30ms/step - accuracy: 0.8391 - loss: 0.4503
[36m(train_cnn_ray_tune pid=1160672)[0m 
[1m 1/43[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m15s[0m 358ms/step
[36m(train_cnn_ray_tune pid=1160672)[0m 
[1m11/43[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 5ms/step   
[1m21/43[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 5ms/step
[36m(train_cnn_ray_tune pid=1160672)[0m 
[1m31/43[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 5ms/step
[36m(train_cnn_ray_tune pid=1160672)[0m 
[1m41/43[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 5ms/step
[36m(train_cnn_ray_tune pid=1160663)[0m 
[1m  1/619[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m37s[0m 60ms/step - accuracy: 0.9375 - loss: 0.3214
[36m(train_cnn_ray_tune pid=1160672)[0m 
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 14ms/step
[36m(train_cnn_ray_tune pid=1160672)[0m 
[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 50ms/step
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[36m(train_cnn_ray_tune pid=1160672)[0m 
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[36m(train_cnn_ray_tune pid=1160677)[0m 
[1m230/310[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m1s[0m 16ms/step - accuracy: 0.7780 - loss: 0.5811[32m [repeated 59x across cluster][0m
[36m(train_cnn_ray_tune pid=1160659)[0m 
[1m155/310[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m2s[0m 19ms/step - accuracy: 0.6451 - loss: 0.8737
[1m158/310[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m2s[0m 19ms/step - accuracy: 0.6451 - loss: 0.8736[32m [repeated 145x across cluster][0m
[36m(train_cnn_ray_tune pid=1160672)[0m 
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[36m(train_cnn_ray_tune pid=1160672)[0m 
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[36m(train_cnn_ray_tune pid=1160672)[0m 
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[1m57/89[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 8ms/step
[36m(train_cnn_ray_tune pid=1160672)[0m 
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[36m(train_cnn_ray_tune pid=1160672)[0m 
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[36m(train_cnn_ray_tune pid=1160672)[0m 
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Trial trial_2e133 finished iteration 1 at 2025-11-08 16:46:14. Total running time: 6min 16s
[36m(train_cnn_ray_tune pid=1160672)[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=1160672)[0m   _log_deprecation_warning([32m [repeated 2x across cluster][0m
╭──────────────────────────────────────╮
│ Trial trial_2e133 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             373.829 │
│ time_total_s                 373.829 │
│ training_iteration                 1 │
│ val_accuracy                 0.69066 │
╰──────────────────────────────────────╯

Trial trial_2e133 completed after 1 iterations at 2025-11-08 16:46:14. Total running time: 6min 16s
[36m(train_cnn_ray_tune pid=1160672)[0m 
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[36m(train_cnn_ray_tune pid=1160663)[0m 
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[36m(train_cnn_ray_tune pid=1160677)[0m Epoch 23/23[32m [repeated 3x across cluster][0m
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[36m(train_cnn_ray_tune pid=1160663)[0m 
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[36m(train_cnn_ray_tune pid=1160659)[0m 
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[36m(train_cnn_ray_tune pid=1160673)[0m 
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[36m(train_cnn_ray_tune pid=1160673)[0m 
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[36m(train_cnn_ray_tune pid=1160673)[0m 
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[36m(train_cnn_ray_tune pid=1160673)[0m Epoch 10/17[32m [repeated 2x across cluster][0m
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[36m(train_cnn_ray_tune pid=1160677)[0m 
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[36m(train_cnn_ray_tune pid=1160677)[0m 
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[36m(train_cnn_ray_tune pid=1160673)[0m 
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[36m(train_cnn_ray_tune pid=1160677)[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=1160677)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=1160659)[0m 
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[36m(train_cnn_ray_tune pid=1160677)[0m 
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[36m(train_cnn_ray_tune pid=1160677)[0m 
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[36m(train_cnn_ray_tune pid=1160677)[0m 
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[36m(train_cnn_ray_tune pid=1160677)[0m 
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[36m(train_cnn_ray_tune pid=1160677)[0m 
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[36m(train_cnn_ray_tune pid=1160677)[0m 
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Trial trial_2e133 finished iteration 1 at 2025-11-08 16:46:24. Total running time: 6min 27s
╭──────────────────────────────────────╮
│ Trial trial_2e133 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s              384.16 │
│ time_total_s                  384.16 │
│ training_iteration                 1 │
│ val_accuracy                 0.68364 │
╰──────────────────────────────────────╯

Trial trial_2e133 completed after 1 iterations at 2025-11-08 16:46:24. Total running time: 6min 27s
[36m(train_cnn_ray_tune pid=1160659)[0m 
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[36m(train_cnn_ray_tune pid=1160659)[0m 
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[36m(train_cnn_ray_tune pid=1160659)[0m 
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[36m(train_cnn_ray_tune pid=1160673)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160659)[0m Epoch 21/27[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1160663)[0m 
[1m 1/43[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m23s[0m 553ms/step
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[36m(train_cnn_ray_tune pid=1160663)[0m 
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 19ms/step
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 20ms/step[32m [repeated 4x across cluster][0m

Trial status: 16 TERMINATED | 4 RUNNING
Current time: 2025-11-08 16:46:28. Total running time: 6min 31s
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_2e133    RUNNING              3   adam            relu                                   32                 32                  5                 1          1.30564e-05         27                                              │
│ trial_2e133    RUNNING              3   adam            relu                                   32                 96                  3                 0          3.11011e-05         20                                              │
│ trial_2e133    RUNNING              3   rmsprop         tanh                                   16                 64                  5                 0          4.29062e-05         17                                              │
│ trial_2e133    RUNNING              3   rmsprop         relu                                   16                 64                  3                 1          0.000108525         28                                              │
│ trial_2e133    TERMINATED           2   rmsprop         relu                                   16                 96                  3                 1          6.07697e-05         25        1            354.604         0.686096 │
│ trial_2e133    TERMINATED           2   adam            tanh                                   16                 96                  3                 1          0.00329571          17        1            360.147         0.715941 │
│ trial_2e133    TERMINATED           2   adam            tanh                                   32                 96                  3                 0          0.000165898         20        1            214.247         0.6875   │
│ trial_2e133    TERMINATED           3   rmsprop         relu                                   32                 64                  5                 1          0.00302727          18        1            314.836         0.71559  │
│ trial_2e133    TERMINATED           2   adam            relu                                   16                 32                  5                 0          8.69755e-05         18        1            373.829         0.69066  │
│ trial_2e133    TERMINATED           3   adam            relu                                   16                 32                  5                 0          0.000149645         28        1            356.311         0.669593 │
│ trial_2e133    TERMINATED           2   rmsprop         relu                                   16                 32                  5                 1          0.00234329          17        1            249.036         0.72191  │
│ trial_2e133    TERMINATED           2   rmsprop         tanh                                   16                 96                  5                 1          0.00031027          18        1            339.39          0.672051 │
│ trial_2e133    TERMINATED           2   adam            tanh                                   16                 64                  3                 0          0.000308353         26        1            282.792         0.717346 │
│ trial_2e133    TERMINATED           3   rmsprop         relu                                   32                 32                  5                 0          0.00111465          17        1            221.951         0.713834 │
│ trial_2e133    TERMINATED           2   rmsprop         tanh                                   16                 32                  5                 0          0.000595899         20        1            327.623         0.69382  │
│ trial_2e133    TERMINATED           2   adam            tanh                                   32                 32                  3                 0          0.000147791         24        1            251.589         0.700492 │
│ trial_2e133    TERMINATED           3   rmsprop         tanh                                   32                 64                  5                 0          2.69047e-05         23        1            342.942         0.618329 │
│ trial_2e133    TERMINATED           2   adam            relu                                   16                 32                  5                 0          0.000225987         26        1            309.671         0.688553 │
│ trial_2e133    TERMINATED           3   rmsprop         tanh                                   32                 64                  5                 0          0.00161065          19        1            335.871         0.703652 │
│ trial_2e133    TERMINATED           2   rmsprop         relu                                   32                 96                  3                 1          1.93582e-05         23        1            384.16          0.683638 │
╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=1160663)[0m 
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[1m 7/89[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 9ms/step 
[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160663)[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=1160663)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=1160659)[0m 
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Trial trial_2e133 finished iteration 1 at 2025-11-08 16:46:29. Total running time: 6min 31s
╭──────────────────────────────────────╮
│ Trial trial_2e133 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             388.665 │
│ time_total_s                 388.665 │
│ training_iteration                 1 │
│ val_accuracy                 0.67942 │
╰──────────────────────────────────────╯

Trial trial_2e133 completed after 1 iterations at 2025-11-08 16:46:29. Total running time: 6min 31s
[36m(train_cnn_ray_tune pid=1160659)[0m 
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[36m(train_cnn_ray_tune pid=1160659)[0m 
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[36m(train_cnn_ray_tune pid=1160659)[0m 
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[36m(train_cnn_ray_tune pid=1160673)[0m 
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[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 39ms/step
[1m10/89[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 6ms/step 

Trial trial_2e133 finished iteration 1 at 2025-11-08 16:46:32. Total running time: 6min 34s
╭──────────────────────────────────────╮
│ Trial trial_2e133 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             391.633 │
│ time_total_s                 391.633 │
│ training_iteration                 1 │
│ val_accuracy                 0.64326 │
╰──────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=1160661)[0m Epoch 16/20[32m [repeated 2x across cluster][0m

Trial trial_2e133 completed after 1 iterations at 2025-11-08 16:46:32. Total running time: 6min 34s
[36m(train_cnn_ray_tune pid=1160673)[0m 
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[36m(train_cnn_ray_tune pid=1160659)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160659)[0m 
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[36m(train_cnn_ray_tune pid=1160659)[0m 
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[36m(train_cnn_ray_tune pid=1160659)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[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=1160661)[0m   _log_deprecation_warning([32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1160659)[0m 
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[36m(train_cnn_ray_tune pid=1160659)[0m 
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[36m(train_cnn_ray_tune pid=1160659)[0m 
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Trial trial_2e133 finished iteration 1 at 2025-11-08 16:46:36. Total running time: 6min 38s
╭──────────────────────────────────────╮
│ Trial trial_2e133 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             395.644 │
│ time_total_s                 395.644 │
│ training_iteration                 1 │
│ val_accuracy                 0.68364 │
╰──────────────────────────────────────╯

Trial trial_2e133 completed after 1 iterations at 2025-11-08 16:46:36. Total running time: 6min 38s
[36m(train_cnn_ray_tune pid=1160659)[0m 
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[36m(train_cnn_ray_tune pid=1160659)[0m 
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[36m(train_cnn_ray_tune pid=1160659)[0m 
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[36m(train_cnn_ray_tune pid=1160659)[0m 
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[36m(train_cnn_ray_tune pid=1160659)[0m 
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[36m(train_cnn_ray_tune pid=1160659)[0m 
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[36m(train_cnn_ray_tune pid=1160659)[0m 
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[36m(train_cnn_ray_tune pid=1160659)[0m 
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[36m(train_cnn_ray_tune pid=1160659)[0m 
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[36m(train_cnn_ray_tune pid=1160659)[0m 
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[36m(train_cnn_ray_tune pid=1160659)[0m 
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[36m(train_cnn_ray_tune pid=1160659)[0m 
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[36m(train_cnn_ray_tune pid=1160659)[0m 
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[36m(train_cnn_ray_tune pid=1160659)[0m 
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[36m(train_cnn_ray_tune pid=1160659)[0m Epoch 24/27[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1160659)[0m 
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[36m(train_cnn_ray_tune pid=1160659)[0m 
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[36m(train_cnn_ray_tune pid=1160659)[0m 
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[36m(train_cnn_ray_tune pid=1160659)[0m 
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[36m(train_cnn_ray_tune pid=1160659)[0m 
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[36m(train_cnn_ray_tune pid=1160659)[0m 
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[36m(train_cnn_ray_tune pid=1160659)[0m 
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[36m(train_cnn_ray_tune pid=1160659)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160659)[0m 
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[36m(train_cnn_ray_tune pid=1160659)[0m 
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[36m(train_cnn_ray_tune pid=1160659)[0m 
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[36m(train_cnn_ray_tune pid=1160659)[0m 
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[36m(train_cnn_ray_tune pid=1160659)[0m 
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[36m(train_cnn_ray_tune pid=1160659)[0m 
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[36m(train_cnn_ray_tune pid=1160659)[0m 
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[36m(train_cnn_ray_tune pid=1160661)[0m 
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[36m(train_cnn_ray_tune pid=1160659)[0m 
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[36m(train_cnn_ray_tune pid=1160659)[0m 
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2025-11-08 16:46:43,145	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_PI/case_PI_CAPTURE24_acc_gyr_superclasses_CPA_METs/CAPTURE24_hyperparameters_tuning' in 0.0055s.
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[36m(train_cnn_ray_tune pid=1160659)[0m 
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[36m(train_cnn_ray_tune pid=1160659)[0m 
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[36m(train_cnn_ray_tune pid=1160659)[0m 
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[36m(train_cnn_ray_tune pid=1160659)[0m Epoch 27/27[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=1160659)[0m 
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Trial trial_2e133 finished iteration 1 at 2025-11-08 16:46:43. Total running time: 6min 45s
╭──────────────────────────────────────╮
│ Trial trial_2e133 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             402.474 │
│ time_total_s                 402.474 │
│ training_iteration                 1 │
│ val_accuracy                 0.60534 │
╰──────────────────────────────────────╯

Trial trial_2e133 completed after 1 iterations at 2025-11-08 16:46:43. Total running time: 6min 45s

Trial status: 20 TERMINATED
Current time: 2025-11-08 16:46:43. Total running time: 6min 45s
Logical resource usage: 1.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
I0000 00:00:1762616803.276684 1159025 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13760 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
[36m(train_cnn_ray_tune pid=1160659)[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=1160659)[0m   _log_deprecation_warning(
╭────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     num_resblocks     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_2e133    TERMINATED           2   rmsprop         relu                                   16                 96                  3                 1          6.07697e-05         25        1            354.604         0.686096 │
│ trial_2e133    TERMINATED           3   adam            relu                                   32                 32                  5                 1          1.30564e-05         27        1            402.474         0.605337 │
│ trial_2e133    TERMINATED           3   adam            relu                                   32                 96                  3                 0          3.11011e-05         20        1            395.645         0.683638 │
│ trial_2e133    TERMINATED           2   adam            tanh                                   16                 96                  3                 1          0.00329571          17        1            360.147         0.715941 │
│ trial_2e133    TERMINATED           2   adam            tanh                                   32                 96                  3                 0          0.000165898         20        1            214.247         0.6875   │
│ trial_2e133    TERMINATED           3   rmsprop         relu                                   32                 64                  5                 1          0.00302727          18        1            314.836         0.71559  │
│ trial_2e133    TERMINATED           2   adam            relu                                   16                 32                  5                 0          8.69755e-05         18        1            373.829         0.69066  │
│ trial_2e133    TERMINATED           3   adam            relu                                   16                 32                  5                 0          0.000149645         28        1            356.311         0.669593 │
│ trial_2e133    TERMINATED           2   rmsprop         relu                                   16                 32                  5                 1          0.00234329          17        1            249.036         0.72191  │
│ trial_2e133    TERMINATED           2   rmsprop         tanh                                   16                 96                  5                 1          0.00031027          18        1            339.39          0.672051 │
│ trial_2e133    TERMINATED           2   adam            tanh                                   16                 64                  3                 0          0.000308353         26        1            282.792         0.717346 │
│ trial_2e133    TERMINATED           3   rmsprop         relu                                   32                 32                  5                 0          0.00111465          17        1            221.951         0.713834 │
│ trial_2e133    TERMINATED           2   rmsprop         tanh                                   16                 32                  5                 0          0.000595899         20        1            327.623         0.69382  │
│ trial_2e133    TERMINATED           2   adam            tanh                                   32                 32                  3                 0          0.000147791         24        1            251.589         0.700492 │
│ trial_2e133    TERMINATED           3   rmsprop         tanh                                   32                 64                  5                 0          2.69047e-05         23        1            342.942         0.618329 │
│ trial_2e133    TERMINATED           2   adam            relu                                   16                 32                  5                 0          0.000225987         26        1            309.671         0.688553 │
│ trial_2e133    TERMINATED           3   rmsprop         tanh                                   16                 64                  5                 0          4.29062e-05         17        1            391.633         0.643258 │
│ trial_2e133    TERMINATED           3   rmsprop         tanh                                   32                 64                  5                 0          0.00161065          19        1            335.871         0.703652 │
│ trial_2e133    TERMINATED           3   rmsprop         relu                                   16                 64                  3                 1          0.000108525         28        1            388.665         0.679424 │
│ trial_2e133    TERMINATED           2   rmsprop         relu                                   32                 96                  3                 1          1.93582e-05         23        1            384.16          0.683638 │
╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

Mejores hiperparámetros: {'N_capas': 2, 'optimizador': 'rmsprop', 'funcion_activacion': 'relu', 'tamanho_minilote': 16, 'numero_filtros': 32, 'tamanho_filtro': 5, 'num_resblocks': 1, 'tasa_aprendizaje': 0.002343288383047448, 'epochs': 17}
Epoch 1/17
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762616805.259120 1190350 service.cc:152] XLA service 0x7c18180229a0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762616805.259181 1190350 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-08 16:46:45.299113: 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 00:00:1762616805.526138 1190350 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762616807.458005 1190350 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 2/17

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Epoch 3/17

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Epoch 4/17

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[1m 94/619[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.7922 - loss: 0.5583
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[1m192/619[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.7851 - loss: 0.5657
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Epoch 5/17

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Epoch 6/17

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Epoch 7/17

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[1m365/619[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.8718 - loss: 0.3724
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[1m568/619[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.8695 - loss: 0.3780
[1m603/619[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.8693 - loss: 0.3787
[1m619/619[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.8693 - loss: 0.3790 - val_accuracy: 0.7156 - val_loss: 0.8638
Epoch 8/17

[1m  1/619[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 20ms/step - accuracy: 0.9375 - loss: 0.1944
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[1m578/619[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.8821 - loss: 0.3459
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Epoch 9/17

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Saved model to disk.
[36m(train_cnn_ray_tune pid=1160659)[0m 
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2025-11-08 16:47:13.899822: 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`.
2025-11-08 16:47:13.911302: 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 00:00:1762616833.925518 1192080 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762616833.929953 1192080 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762616833.940580 1192080 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762616833.940604 1192080 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762616833.940606 1192080 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762616833.940608 1192080 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-08 16:47:13.944063: 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 00:00:1762616836.312187 1192080 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13760 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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Epoch 1/17
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762616838.116634 1192188 service.cc:152] XLA service 0x76c5b400fc40 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762616838.116684 1192188 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-08 16:47:18.158135: 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 00:00:1762616838.380911 1192188 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762616840.283103 1192188 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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[1m512/619[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5534 - loss: 1.0743
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Epoch 2/17

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[1m 96/619[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6633 - loss: 0.7713
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Epoch 3/17

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[1m 96/619[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6975 - loss: 0.6748
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[1m191/619[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6942 - loss: 0.6761
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Epoch 4/17

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Epoch 5/17

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[1m 98/619[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.8013 - loss: 0.4838
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[1m231/619[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.8050 - loss: 0.4900
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Epoch 6/17

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[1m133/619[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.8416 - loss: 0.4208
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Epoch 7/17

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Epoch 8/17

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Epoch 9/17

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Epoch 10/17

[1m  1/619[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 19ms/step - accuracy: 1.0000 - loss: 0.0956
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[1m 95/619[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.9244 - loss: 0.2485
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[1m199/619[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.9217 - loss: 0.2517
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[1m 1/43[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m38s[0m 923ms/step
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 20ms/step  
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 20ms/step
Saved model to disk.

=== EJECUCIÓN 1 ===

--- TRAIN (ejecución 1) ---

--- TEST (ejecución 1) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (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.
(1349, 6, 250)
(9904, 6, 250)

[1m  1/310[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5:49[0m 1s/step
[1m 51/310[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step 
[1m106/310[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 956us/step
[1m164/310[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 926us/step
[1m228/310[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 888us/step
[1m288/310[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 880us/step
[1m310/310[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step  
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[1m 1/43[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 20ms/step
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 17ms/step
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 18ms/step

[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m61/89[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 842us/step
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 995us/step
Global accuracy score (validation) = 70.72 [%]
Global F1 score (validation) = 71.25 [%]
[[0.]
 [0.]
 [0.]
 ...
 [3.]
 [3.]
 [3.]]
(1349, 1)
[[7.6975137e-01 2.2599725e-01 1.1189587e-03 3.1324059e-03]
 [9.9765992e-01 2.2975595e-03 4.1598960e-05 9.3830482e-07]
 [8.8730794e-01 9.3499519e-02 1.0270056e-02 8.9225546e-03]
 ...
 [1.8753468e-04 2.2621442e-05 5.5864274e-07 9.9978924e-01]
 [2.0397666e-03 2.7960786e-04 8.2196486e-05 9.9759847e-01]
 [2.7449052e-03 3.7752423e-03 9.9050635e-01 2.9734918e-03]]
(1349, 4)
-------------------------------------------------

Global accuracy score (train) = 87.42 [%]
Global accuracy score (test) = 74.5 [%]
Global F1 score (train) = 87.61 [%]
Global F1 score (test) = 75.0 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.56      0.62      0.59       350
MODERATE-INTENSITY       0.59      0.53      0.56       350
         SEDENTARY       0.96      0.96      0.96       350
VIGOROUS-INTENSITY       0.89      0.90      0.89       299

          accuracy                           0.74      1349
         macro avg       0.75      0.75      0.75      1349
      weighted avg       0.75      0.74      0.74      1349


Accuracy capturado en la ejecución 1: 74.5 [%]
F1-score capturado en la ejecución 1: 75.0 [%]

=== EJECUCIÓN 2 ===

--- TRAIN (ejecución 2) ---

--- TEST (ejecución 2) ---
2025-11-08 16:47:45.565176: 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`.
2025-11-08 16:47:45.576636: 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 00:00:1762616865.589987 1193992 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762616865.594204 1193992 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762616865.604314 1193992 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762616865.604339 1193992 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762616865.604348 1193992 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762616865.604349 1193992 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-08 16:47:45.607619: 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 00:00:1762616867.955391 1193992 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13760 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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Epoch 1/17
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762616869.736816 1194102 service.cc:152] XLA service 0x74f92c004590 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762616869.736858 1194102 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-08 16:47:49.775948: 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 00:00:1762616869.990637 1194102 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762616871.908657 1194102 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/619[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m33:32[0m 3s/step - accuracy: 0.2500 - loss: 2.3359
[1m 24/619[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.4318 - loss: 1.5776  
[1m 55/619[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.4702 - loss: 1.4125
[1m 90/619[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4881 - loss: 1.3272
[1m125/619[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5020 - loss: 1.2736
[1m157/619[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5125 - loss: 1.2385
[1m189/619[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5210 - loss: 1.2119
[1m219/619[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5272 - loss: 1.1917
[1m251/619[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5330 - loss: 1.1729
[1m284/619[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5382 - loss: 1.1563
[1m317/619[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5427 - loss: 1.1416
[1m349/619[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5467 - loss: 1.1289
[1m382/619[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5503 - loss: 1.1177
[1m412/619[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5533 - loss: 1.1083
[1m446/619[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5564 - loss: 1.0983
[1m477/619[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5590 - loss: 1.0897
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Epoch 2/17

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Epoch 3/17

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[1m 99/619[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6995 - loss: 0.6824
[1m133/619[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.7079 - loss: 0.6702
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[1m231/619[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.7156 - loss: 0.6589
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Epoch 4/17

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Epoch 5/17

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Epoch 6/17

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Epoch 7/17

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Epoch 8/17

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[1m 95/619[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.8717 - loss: 0.3658
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[1m192/619[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.8770 - loss: 0.3480
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[1m548/619[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.8771 - loss: 0.3525
[1m579/619[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.8770 - loss: 0.3530
[1m609/619[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.8770 - loss: 0.3536
[1m619/619[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.8770 - loss: 0.3538 - val_accuracy: 0.7310 - val_loss: 0.7441
Epoch 9/17

[1m  1/619[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 20ms/step - accuracy: 0.9375 - loss: 0.1888
[1m 29/619[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.9083 - loss: 0.3151  
[1m 58/619[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.9065 - loss: 0.3034
[1m 92/619[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.9054 - loss: 0.3009
[1m124/619[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.9023 - loss: 0.3076
[1m156/619[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.9003 - loss: 0.3122
[1m189/619[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.8990 - loss: 0.3153
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[1m289/619[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.8979 - loss: 0.3170
[1m321/619[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.8974 - loss: 0.3179
[1m352/619[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.8971 - loss: 0.3183
[1m386/619[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.8967 - loss: 0.3191
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[1m619/619[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.8944 - loss: 0.3242 - val_accuracy: 0.7240 - val_loss: 0.8429

[1m 1/43[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m38s[0m 912ms/step
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 20ms/step  
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 21ms/step
Saved model to disk.
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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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)
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This activity can't be balanced (in a downsampling way)
Loaded model from disk.
(1349, 6, 250)
(9904, 6, 250)

[1m  1/310[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5:41[0m 1s/step
[1m 55/310[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 939us/step
[1m114/310[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 892us/step
[1m172/310[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 882us/step
[1m232/310[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 871us/step
[1m290/310[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 871us/step
[1m310/310[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step  
[1m310/310[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 4ms/step

[1m 1/43[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 16ms/step
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 17ms/step
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 17ms/step

[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m53/89[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 973us/step
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step  
Global accuracy score (validation) = 71.88 [%]
Global F1 score (validation) = 72.5 [%]
[[0.]
 [0.]
 [0.]
 ...
 [3.]
 [3.]
 [3.]]
(1349, 1)
[[7.1903920e-01 2.7998909e-01 3.5670897e-04 6.1490858e-04]
 [4.6139494e-01 5.3639400e-01 1.2468453e-03 9.6427475e-04]
 [9.4600999e-01 5.1133905e-02 1.1343749e-03 1.7217904e-03]
 ...
 [3.2036519e-03 4.3676705e-03 1.6295629e-03 9.9079919e-01]
 [7.0683760e-05 5.3061402e-05 2.2144832e-04 9.9965489e-01]
 [1.9369327e-02 1.8274505e-02 7.4620241e-01 2.1615379e-01]]
(1349, 4)
-------------------------------------------------

Global accuracy score (train) = 88.14 [%]
Global accuracy score (test) = 77.02 [%]
Global F1 score (train) = 88.33 [%]
Global F1 score (test) = 77.93 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.60      0.69      0.65       350
MODERATE-INTENSITY       0.62      0.61      0.61       350
         SEDENTARY       0.97      0.94      0.95       350
VIGOROUS-INTENSITY       0.96      0.86      0.91       299

          accuracy                           0.77      1349
         macro avg       0.79      0.77      0.78      1349
      weighted avg       0.78      0.77      0.77      1349


Accuracy capturado en la ejecución 2: 77.02 [%]
F1-score capturado en la ejecución 2: 77.93 [%]

=== EJECUCIÓN 3 ===

--- TRAIN (ejecución 3) ---

--- TEST (ejecución 3) ---
2025-11-08 16:48:16.059519: 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`.
2025-11-08 16:48:16.070875: 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 00:00:1762616896.083975 1195811 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762616896.088100 1195811 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762616896.097974 1195811 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762616896.097991 1195811 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762616896.097994 1195811 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762616896.097995 1195811 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-08 16:48:16.101225: 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 00:00:1762616898.422071 1195811 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13760 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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Epoch 1/17
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762616900.207534 1195925 service.cc:152] XLA service 0x7226e4007ee0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762616900.207602 1195925 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-08 16:48:20.249148: 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 00:00:1762616900.464329 1195925 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762616902.381689 1195925 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 2/17

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Epoch 3/17

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[1m 31/619[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.7208 - loss: 0.6274  
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Epoch 4/17

[1m  1/619[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 21ms/step - accuracy: 0.7500 - loss: 0.6308
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[1m191/619[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.7783 - loss: 0.5546
[1m224/619[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.7774 - loss: 0.5555
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Epoch 5/17

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[1m133/619[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.7798 - loss: 0.5281
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[1m199/619[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.7816 - loss: 0.5285
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Epoch 6/17

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[1m365/619[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.8328 - loss: 0.4481
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Epoch 7/17

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[1m 98/619[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.8691 - loss: 0.3869
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[1m 1/43[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m39s[0m 932ms/step
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 20ms/step  
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Saved model to disk.
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Loaded model from disk.
(1349, 6, 250)
(9904, 6, 250)

[1m  1/310[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5:47[0m 1s/step
[1m 54/310[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 954us/step
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[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 17ms/step
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[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m59/89[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 873us/step
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step  
Global accuracy score (validation) = 71.91 [%]
Global F1 score (validation) = 72.53 [%]
[[0.]
 [0.]
 [0.]
 ...
 [3.]
 [3.]
 [3.]]
(1349, 1)
[[8.0220884e-01 1.9464628e-01 2.2057735e-03 9.3906338e-04]
 [9.1895550e-01 7.3610425e-02 5.5024913e-03 1.9315956e-03]
 [8.8913500e-01 1.0085501e-01 8.2460539e-03 1.7639081e-03]
 ...
 [2.9725724e-04 4.1294112e-05 3.9054130e-06 9.9965751e-01]
 [5.5449456e-04 1.7089355e-04 3.6078916e-05 9.9923849e-01]
 [1.5913989e-02 2.9606547e-02 9.4005477e-01 1.4424726e-02]]
(1349, 4)
-------------------------------------------------

Global accuracy score (train) = 85.51 [%]
Global accuracy score (test) = 71.76 [%]
Global F1 score (train) = 85.65 [%]
Global F1 score (test) = 72.5 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.55      0.68      0.61       350
MODERATE-INTENSITY       0.59      0.52      0.55       350
         SEDENTARY       0.97      0.85      0.91       350
VIGOROUS-INTENSITY       0.82      0.84      0.83       299

          accuracy                           0.72      1349
         macro avg       0.73      0.72      0.73      1349
      weighted avg       0.73      0.72      0.72      1349


Accuracy capturado en la ejecución 3: 71.76 [%]
F1-score capturado en la ejecución 3: 72.5 [%]

=== EJECUCIÓN 4 ===

--- TRAIN (ejecución 4) ---

--- TEST (ejecución 4) ---
2025-11-08 16:48:44.140404: 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`.
2025-11-08 16:48:44.151583: 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 00:00:1762616924.164456 1197411 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762616924.168399 1197411 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762616924.178282 1197411 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762616924.178302 1197411 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762616924.178304 1197411 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762616924.178306 1197411 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-08 16:48:44.181432: 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 00:00:1762616926.541704 1197411 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13760 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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Epoch 1/17
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762616928.312002 1197550 service.cc:152] XLA service 0x73eb800127f0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762616928.312035 1197550 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-08 16:48:48.346912: 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 00:00:1762616928.572383 1197550 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762616930.492638 1197550 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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[1m294/619[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5465 - loss: 1.1566
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Epoch 2/17

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[1m 96/619[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6834 - loss: 0.7692
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[1m231/619[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6860 - loss: 0.7639
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[1m537/619[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6870 - loss: 0.7519
[1m571/619[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.6872 - loss: 0.7505
[1m603/619[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.6873 - loss: 0.7494
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Epoch 3/17

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[1m 96/619[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.7145 - loss: 0.6665
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Epoch 4/17

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Epoch 5/17

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[1m 98/619[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.8245 - loss: 0.4749
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[1m201/619[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.8180 - loss: 0.4836
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Epoch 6/17

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[1m 31/619[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.8363 - loss: 0.4671  
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Epoch 7/17

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[1m 62/619[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.8933 - loss: 0.3385
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Epoch 8/17

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[1m 32/619[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.8891 - loss: 0.3592  
[1m 66/619[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.8921 - loss: 0.3332
[1m 99/619[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.8913 - loss: 0.3323
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[1m201/619[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.8916 - loss: 0.3321
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[1m270/619[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.8924 - loss: 0.3316
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[1m338/619[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.8925 - loss: 0.3314
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[1m564/619[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.8921 - loss: 0.3325
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Epoch 9/17

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[1m 98/619[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.9090 - loss: 0.2869
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Epoch 10/17

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[1m 96/619[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.9184 - loss: 0.2610
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[1m 1/43[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m38s[0m 928ms/step
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 20ms/step  
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Saved model to disk.
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Loaded model from disk.
(1349, 6, 250)
(9904, 6, 250)

[1m  1/310[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5:52[0m 1s/step
[1m 63/310[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 818us/step
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[1m255/310[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 798us/step
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[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 18ms/step
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 18ms/step

[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m56/89[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 927us/step
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step  
Global accuracy score (validation) = 72.05 [%]
Global F1 score (validation) = 72.81 [%]
[[0.]
 [0.]
 [0.]
 ...
 [3.]
 [3.]
 [3.]]
(1349, 1)
[[8.5879904e-01 1.3976413e-01 1.1836933e-04 1.3184636e-03]
 [9.7799532e-02 8.9555687e-01 3.6434850e-03 3.0000799e-03]
 [9.2278606e-01 7.4928947e-02 2.6176972e-04 2.0231772e-03]
 ...
 [3.3688364e-05 6.8371542e-06 5.4394691e-06 9.9995410e-01]
 [2.4426187e-04 1.0495217e-04 1.0594655e-04 9.9954480e-01]
 [1.5192619e-03 3.5306013e-03 9.8733491e-01 7.6151863e-03]]
(1349, 4)
-------------------------------------------------

Global accuracy score (train) = 85.61 [%]
Global accuracy score (test) = 73.54 [%]
Global F1 score (train) = 85.94 [%]
Global F1 score (test) = 74.47 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.62      0.60      0.61       350
MODERATE-INTENSITY       0.54      0.62      0.58       350
         SEDENTARY       0.98      0.92      0.95       350
VIGOROUS-INTENSITY       0.89      0.81      0.84       299

          accuracy                           0.74      1349
         macro avg       0.75      0.74      0.74      1349
      weighted avg       0.75      0.74      0.74      1349


Accuracy capturado en la ejecución 4: 73.54 [%]
F1-score capturado en la ejecución 4: 74.47 [%]

=== EJECUCIÓN 5 ===

--- TRAIN (ejecución 5) ---

--- TEST (ejecución 5) ---
2025-11-08 16:49:15.825202: 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`.
2025-11-08 16:49:15.836536: 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 00:00:1762616955.849565 1199350 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762616955.853689 1199350 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762616955.863532 1199350 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762616955.863550 1199350 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762616955.863553 1199350 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762616955.863554 1199350 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-08 16:49:15.866664: 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 00:00:1762616958.208636 1199350 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13760 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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Epoch 1/17
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762616959.983297 1199471 service.cc:152] XLA service 0x73c088017c40 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762616959.983323 1199471 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-08 16:49:20.022663: 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 00:00:1762616960.246394 1199471 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762616962.166152 1199471 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 95/619[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4536 - loss: 1.3261
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[1m231/619[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5012 - loss: 1.1973
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[1m365/619[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5214 - loss: 1.1393
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Epoch 2/17

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[1m 99/619[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6362 - loss: 0.8024
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[1m198/619[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6527 - loss: 0.7836
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[1m298/619[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6587 - loss: 0.7727
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Epoch 3/17

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[1m 68/619[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6928 - loss: 0.6657
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Epoch 4/17

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Epoch 5/17

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Epoch 6/17

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[1m365/619[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.8252 - loss: 0.4532
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Epoch 7/17

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[1m187/619[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.8516 - loss: 0.3966
[1m221/619[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.8528 - loss: 0.3972
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Epoch 8/17

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Epoch 9/17

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[1m 95/619[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.8943 - loss: 0.3225
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Epoch 10/17

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[1m 93/619[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.9158 - loss: 0.2361
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[1m 1/43[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m38s[0m 914ms/step
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 21ms/step  
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Saved model to disk.
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Loaded model from disk.
(1349, 6, 250)
(9904, 6, 250)

[1m  1/310[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5:44[0m 1s/step
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[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 17ms/step
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 18ms/step

[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 15ms/step
[1m50/89[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step 
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step
Global accuracy score (validation) = 73.6 [%]
Global F1 score (validation) = 74.24 [%]
[[0.]
 [0.]
 [0.]
 ...
 [3.]
 [3.]
 [3.]]
(1349, 1)
[[5.9331205e-02 9.3350857e-01 4.0989965e-03 3.0611309e-03]
 [9.9898094e-01 6.3959317e-04 7.1859217e-06 3.7228965e-04]
 [7.2199684e-01 2.6635191e-01 1.7540883e-03 9.8972023e-03]
 ...
 [4.8319309e-04 9.4099501e-05 3.9042472e-05 9.9938357e-01]
 [4.6974752e-04 3.1749820e-04 9.0612964e-05 9.9912208e-01]
 [2.8250057e-02 3.6967020e-02 6.7529935e-01 2.5948361e-01]]
(1349, 4)
-------------------------------------------------

Global accuracy score (train) = 87.65 [%]
Global accuracy score (test) = 74.13 [%]
Global F1 score (train) = 87.85 [%]
Global F1 score (test) = 74.83 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.59      0.59      0.59       350
MODERATE-INTENSITY       0.57      0.60      0.58       350
         SEDENTARY       0.95      0.94      0.95       350
VIGOROUS-INTENSITY       0.89      0.84      0.87       299

          accuracy                           0.74      1349
         macro avg       0.75      0.74      0.75      1349
      weighted avg       0.75      0.74      0.74      1349


Accuracy capturado en la ejecución 5: 74.13 [%]
F1-score capturado en la ejecución 5: 74.83 [%]

=== EJECUCIÓN 6 ===

--- TRAIN (ejecución 6) ---

--- TEST (ejecución 6) ---
2025-11-08 16:49:47.571980: 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`.
2025-11-08 16:49:47.583446: 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 00:00:1762616987.596743 1201275 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762616987.600884 1201275 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762616987.610689 1201275 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762616987.610708 1201275 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762616987.610710 1201275 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762616987.610711 1201275 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-08 16:49:47.613915: 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 00:00:1762616989.968150 1201275 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13760 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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Epoch 1/17
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762616991.747691 1201415 service.cc:152] XLA service 0x723358025a70 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762616991.747721 1201415 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-08 16:49:51.782073: 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 00:00:1762616992.010023 1201415 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762616993.930166 1201415 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 2/17

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[1m586/619[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.6707 - loss: 0.7571
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Epoch 3/17

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Epoch 4/17

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Epoch 5/17

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[1m 31/619[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.7923 - loss: 0.5160  
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Epoch 6/17

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Epoch 7/17

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[1m199/619[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.8648 - loss: 0.3863
[1m231/619[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.8647 - loss: 0.3874
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[1m298/619[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.8631 - loss: 0.3919
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[1m365/619[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.8616 - loss: 0.3959
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Epoch 8/17

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Epoch 9/17

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[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 21ms/step  
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Saved model to disk.
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
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Loaded model from disk.
(1349, 6, 250)
(9904, 6, 250)

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[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m59/89[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 870us/step
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step  
Global accuracy score (validation) = 71.1 [%]
Global F1 score (validation) = 71.66 [%]
[[0.]
 [0.]
 [0.]
 ...
 [3.]
 [3.]
 [3.]]
(1349, 1)
[[7.6985186e-01 2.2395912e-01 1.7600040e-03 4.4290191e-03]
 [7.0673031e-01 2.8242067e-01 6.9486664e-04 1.0154134e-02]
 [9.5447475e-01 4.0983889e-02 3.1480261e-03 1.3933909e-03]
 ...
 [9.6745690e-04 1.2699100e-03 5.5922574e-05 9.9770677e-01]
 [1.1057544e-03 9.6656784e-04 1.5386030e-04 9.9777383e-01]
 [3.7390622e-03 6.7632003e-03 9.6649152e-01 2.3006270e-02]]
(1349, 4)
-------------------------------------------------

Global accuracy score (train) = 87.83 [%]
Global accuracy score (test) = 70.2 [%]
Global F1 score (train) = 88.0 [%]
Global F1 score (test) = 71.65 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.51      0.62      0.56       350
MODERATE-INTENSITY       0.54      0.53      0.54       350
         SEDENTARY       0.97      0.85      0.91       350
VIGOROUS-INTENSITY       0.91      0.83      0.87       299

          accuracy                           0.70      1349
         macro avg       0.73      0.71      0.72      1349
      weighted avg       0.73      0.70      0.71      1349


Accuracy capturado en la ejecución 6: 70.2 [%]
F1-score capturado en la ejecución 6: 71.65 [%]

=== EJECUCIÓN 7 ===

--- TRAIN (ejecución 7) ---

--- TEST (ejecución 7) ---
2025-11-08 16:50:18.058500: 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`.
2025-11-08 16:50:18.069778: 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 00:00:1762617018.084088 1203103 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762617018.088319 1203103 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762617018.098959 1203103 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762617018.098981 1203103 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762617018.098983 1203103 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762617018.098985 1203103 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-08 16:50:18.102115: 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 00:00:1762617020.477066 1203103 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13760 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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Epoch 1/17
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762617022.250903 1203233 service.cc:152] XLA service 0x7cdf30013ea0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762617022.250950 1203233 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-08 16:50:22.290969: 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 00:00:1762617022.515074 1203233 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762617024.455270 1203233 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 2/17

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Epoch 3/17

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Epoch 4/17

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Epoch 5/17

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Epoch 6/17

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[1m 98/619[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.8384 - loss: 0.4569
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[1m298/619[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.8312 - loss: 0.4587
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Epoch 7/17

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[1m 1/43[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m40s[0m 960ms/step
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 20ms/step  
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 21ms/step
Saved model to disk.
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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.
(1349, 6, 250)
(9904, 6, 250)

[1m  1/310[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5:51[0m 1s/step
[1m 52/310[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 994us/step
[1m112/310[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 914us/step
[1m172/310[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 890us/step
[1m233/310[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 871us/step
[1m288/310[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 880us/step
[1m310/310[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step  
[1m310/310[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 4ms/step

[1m 1/43[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 16ms/step
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 18ms/step
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 18ms/step

[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m54/89[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 950us/step
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step  
Global accuracy score (validation) = 72.96 [%]
Global F1 score (validation) = 73.66 [%]
[[0.]
 [0.]
 [0.]
 ...
 [3.]
 [3.]
 [3.]]
(1349, 1)
[[8.9131904e-01 8.7459147e-02 8.0559021e-03 1.3165876e-02]
 [2.2614999e-01 7.6534992e-01 5.1293559e-03 3.3707640e-03]
 [1.3247345e-01 8.5220116e-01 6.2250686e-03 9.1004064e-03]
 ...
 [5.5483575e-03 4.4492665e-03 1.5574687e-03 9.8844492e-01]
 [3.5873320e-04 9.0799847e-04 6.4286659e-04 9.9809045e-01]
 [6.6781953e-02 6.4383447e-02 8.5150558e-01 1.7329056e-02]]
(1349, 4)
-------------------------------------------------

Global accuracy score (train) = 86.06 [%]
Global accuracy score (test) = 70.5 [%]
Global F1 score (train) = 86.46 [%]
Global F1 score (test) = 72.0 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.50      0.47      0.49       350
MODERATE-INTENSITY       0.52      0.67      0.59       350
         SEDENTARY       0.97      0.85      0.91       350
VIGOROUS-INTENSITY       0.96      0.85      0.90       299

          accuracy                           0.70      1349
         macro avg       0.74      0.71      0.72      1349
      weighted avg       0.73      0.70      0.71      1349


Accuracy capturado en la ejecución 7: 70.5 [%]
F1-score capturado en la ejecución 7: 72.0 [%]

=== EJECUCIÓN 8 ===

--- TRAIN (ejecución 8) ---

--- TEST (ejecución 8) ---
2025-11-08 16:50:46.290896: 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`.
2025-11-08 16:50:46.302561: 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 00:00:1762617046.315639 1204726 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762617046.319721 1204726 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762617046.329490 1204726 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762617046.329516 1204726 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762617046.329518 1204726 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762617046.329519 1204726 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-08 16:50:46.332658: 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 00:00:1762617048.652557 1204726 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13760 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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Epoch 1/17
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762617050.393465 1204834 service.cc:152] XLA service 0x782e9400ff10 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762617050.393514 1204834 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-08 16:50:50.432621: 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 00:00:1762617050.648206 1204834 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762617052.566423 1204834 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 2/17

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Epoch 3/17

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Epoch 4/17

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[1m231/619[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.7555 - loss: 0.5642
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[1m298/619[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.7556 - loss: 0.5658
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Epoch 5/17

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Epoch 6/17

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[1m 98/619[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.8260 - loss: 0.4603
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[1m199/619[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.8209 - loss: 0.4707
[1m231/619[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.8206 - loss: 0.4705
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[1m298/619[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.8204 - loss: 0.4700
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Epoch 7/17

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[1m 31/619[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.8820 - loss: 0.3183  
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[1m574/619[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.8557 - loss: 0.3959
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[1m619/619[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.8552 - loss: 0.3970 - val_accuracy: 0.7110 - val_loss: 0.8326
Epoch 8/17

[1m  1/619[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 19ms/step - accuracy: 0.8750 - loss: 0.4329
[1m 36/619[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.8760 - loss: 0.3520  
[1m 69/619[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.8773 - loss: 0.3457
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[1m138/619[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.8753 - loss: 0.3479
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[1m272/619[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.8743 - loss: 0.3540
[1m306/619[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.8741 - loss: 0.3560
[1m338/619[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.8741 - loss: 0.3572
[1m373/619[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.8740 - loss: 0.3583
[1m405/619[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.8738 - loss: 0.3590
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[1m601/619[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.8726 - loss: 0.3624
[1m619/619[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.8725 - loss: 0.3626 - val_accuracy: 0.7258 - val_loss: 0.8089

[1m 1/43[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m38s[0m 928ms/step
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 20ms/step  
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 21ms/step
Saved model to disk.
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (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.
(1349, 6, 250)
(9904, 6, 250)

[1m  1/310[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5:49[0m 1s/step
[1m 52/310[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 984us/step
[1m108/310[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 944us/step
[1m165/310[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 925us/step
[1m226/310[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 898us/step
[1m280/310[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 907us/step
[1m310/310[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step  
[1m310/310[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 4ms/step

[1m 1/43[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 17ms/step
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 18ms/step
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 19ms/step

[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 15ms/step
[1m59/89[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 893us/step
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step  
Global accuracy score (validation) = 71.84 [%]
Global F1 score (validation) = 72.53 [%]
[[0.]
 [0.]
 [0.]
 ...
 [3.]
 [3.]
 [3.]]
(1349, 1)
[[7.6023889e-01 2.3564541e-01 2.0326583e-03 2.0829800e-03]
 [8.6177939e-01 1.2094674e-01 7.6067429e-03 9.6671591e-03]
 [9.8722839e-01 1.2645323e-02 1.0700433e-04 1.9331377e-05]
 ...
 [5.3724897e-04 3.5828730e-04 1.9523290e-05 9.9908495e-01]
 [2.3373838e-03 2.1347709e-03 2.5304413e-04 9.9527478e-01]
 [9.3498565e-03 5.7090600e-03 2.5256859e-02 9.5968419e-01]]
(1349, 4)
-------------------------------------------------

Global accuracy score (train) = 84.95 [%]
Global accuracy score (test) = 74.5 [%]
Global F1 score (train) = 85.18 [%]
Global F1 score (test) = 75.12 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.67      0.52      0.59       350
MODERATE-INTENSITY       0.56      0.76      0.65       350
         SEDENTARY       0.97      0.87      0.92       350
VIGOROUS-INTENSITY       0.86      0.84      0.85       299

          accuracy                           0.74      1349
         macro avg       0.77      0.75      0.75      1349
      weighted avg       0.76      0.74      0.75      1349


Accuracy capturado en la ejecución 8: 74.5 [%]
F1-score capturado en la ejecución 8: 75.12 [%]

=== EJECUCIÓN 9 ===

--- TRAIN (ejecución 9) ---

--- TEST (ejecución 9) ---
2025-11-08 16:51:15.527455: 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`.
2025-11-08 16:51:15.538973: 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 00:00:1762617075.552030 1206426 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762617075.556183 1206426 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762617075.565975 1206426 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762617075.565995 1206426 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762617075.565997 1206426 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762617075.565999 1206426 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-08 16:51:15.569200: 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 00:00:1762617077.925581 1206426 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13760 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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Epoch 1/17
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762617079.705283 1206558 service.cc:152] XLA service 0x7c3fa8010b80 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762617079.705333 1206558 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-08 16:51:19.745990: 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 00:00:1762617079.970160 1206558 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762617081.906583 1206558 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 2/17

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Epoch 3/17

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[1m 31/619[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.7133 - loss: 0.6344  
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Epoch 4/17

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[1m101/619[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.7594 - loss: 0.5800
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[1m168/619[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.7613 - loss: 0.5808
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[1m298/619[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.7634 - loss: 0.5796
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Epoch 5/17

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[1m 93/619[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.8081 - loss: 0.4789
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Epoch 6/17

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[1m291/619[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.8315 - loss: 0.4527
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Epoch 7/17

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[1m 31/619[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.8712 - loss: 0.3517  
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Epoch 8/17

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[1m 1/43[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m39s[0m 930ms/step
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 20ms/step  
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 21ms/step
Saved model to disk.
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (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.
(1349, 6, 250)
(9904, 6, 250)

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[1m 1/43[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 16ms/step
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 18ms/step
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 18ms/step

[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m47/89[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step 
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step
Global accuracy score (validation) = 71.98 [%]
Global F1 score (validation) = 72.62 [%]
[[0.]
 [0.]
 [0.]
 ...
 [3.]
 [3.]
 [3.]]
(1349, 1)
[[9.6510947e-01 2.3110092e-02 4.1569252e-03 7.6235412e-03]
 [8.7483329e-01 8.9334138e-02 1.8238921e-02 1.7593637e-02]
 [9.6581984e-01 2.2489376e-02 4.1232132e-03 7.5675300e-03]
 ...
 [9.9740422e-04 1.1212004e-03 1.7620296e-05 9.9786383e-01]
 [5.7857932e-04 9.6330611e-04 9.9260869e-06 9.9844825e-01]
 [6.7116390e-03 1.3664095e-02 8.0225104e-01 1.7737322e-01]]
(1349, 4)
-------------------------------------------------

Global accuracy score (train) = 86.46 [%]
Global accuracy score (test) = 73.17 [%]
Global F1 score (train) = 86.6 [%]
Global F1 score (test) = 74.01 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.59      0.57      0.58       350
MODERATE-INTENSITY       0.56      0.63      0.60       350
         SEDENTARY       0.96      0.89      0.92       350
VIGOROUS-INTENSITY       0.87      0.86      0.86       299

          accuracy                           0.73      1349
         macro avg       0.75      0.74      0.74      1349
      weighted avg       0.74      0.73      0.74      1349


Accuracy capturado en la ejecución 9: 73.17 [%]
F1-score capturado en la ejecución 9: 74.01 [%]

=== EJECUCIÓN 10 ===

--- TRAIN (ejecución 10) ---

--- TEST (ejecución 10) ---
2025-11-08 16:51:44.939796: 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`.
2025-11-08 16:51:44.951187: 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 00:00:1762617104.964429 1208145 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762617104.968681 1208145 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762617104.978582 1208145 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762617104.978602 1208145 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762617104.978604 1208145 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762617104.978606 1208145 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-08 16:51:44.981811: 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 00:00:1762617107.360798 1208145 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13760 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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Epoch 1/17
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762617109.137042 1208268 service.cc:152] XLA service 0x738968010460 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762617109.137075 1208268 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-08 16:51:49.172362: 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 00:00:1762617109.395843 1208268 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762617111.314426 1208268 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/619[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m33:49[0m 3s/step - accuracy: 0.5000 - loss: 1.1924
[1m 26/619[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.4609 - loss: 1.4907  
[1m 58/619[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.4888 - loss: 1.3825
[1m 90/619[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5012 - loss: 1.3224
[1m122/619[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5102 - loss: 1.2761
[1m153/619[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5164 - loss: 1.2452
[1m180/619[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5210 - loss: 1.2232
[1m210/619[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5253 - loss: 1.2034
[1m246/619[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5298 - loss: 1.1840
[1m279/619[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5335 - loss: 1.1685
[1m315/619[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5372 - loss: 1.1536
[1m347/619[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5407 - loss: 1.1413
[1m376/619[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5440 - loss: 1.1307
[1m407/619[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5472 - loss: 1.1205
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Epoch 2/17

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Epoch 3/17

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[1m192/619[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.7016 - loss: 0.6609
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[1m451/619[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.7086 - loss: 0.6518
[1m485/619[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.7089 - loss: 0.6509
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[1m585/619[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.7097 - loss: 0.6497
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Epoch 4/17

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[1m 97/619[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.7704 - loss: 0.5529
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Epoch 5/17

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Epoch 6/17

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[1m 99/619[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.8189 - loss: 0.4802
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Epoch 7/17

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[1m 99/619[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.8686 - loss: 0.3678
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Epoch 8/17

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[1m 31/619[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.8793 - loss: 0.3961  
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[1m 98/619[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.8794 - loss: 0.3709
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[1m271/619[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.8778 - loss: 0.3717
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Epoch 9/17

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[1m 1/43[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m39s[0m 946ms/step
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Saved model to disk.
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Loaded model from disk.
(1349, 6, 250)
(9904, 6, 250)

[1m  1/310[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5:49[0m 1s/step
[1m 57/310[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 897us/step
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[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 18ms/step
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[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m57/89[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 913us/step
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step  
Global accuracy score (validation) = 72.26 [%]
Global F1 score (validation) = 72.83 [%]
[[0.]
 [0.]
 [0.]
 ...
 [3.]
 [3.]
 [3.]]
(1349, 1)
[[4.1094601e-01 5.8406913e-01 4.9672299e-04 4.4880998e-03]
 [8.2009876e-01 1.6560957e-01 4.6219197e-03 9.6697342e-03]
 [8.2451260e-01 1.6009983e-01 5.1277429e-03 1.0259787e-02]
 ...
 [4.5018570e-04 1.8528182e-04 3.5503243e-05 9.9932903e-01]
 [4.4404544e-04 3.8189188e-04 1.9409954e-05 9.9915469e-01]
 [2.7080509e-03 8.1361085e-03 9.8714679e-01 2.0089261e-03]]
(1349, 4)
-------------------------------------------------

Global accuracy score (train) = 86.5 [%]
Global accuracy score (test) = 75.69 [%]
Global F1 score (train) = 86.71 [%]
Global F1 score (test) = 76.46 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.62      0.59      0.60       350
MODERATE-INTENSITY       0.56      0.64      0.60       350
         SEDENTARY       0.96      0.97      0.97       350
VIGOROUS-INTENSITY       0.94      0.85      0.89       299

          accuracy                           0.76      1349
         macro avg       0.77      0.76      0.76      1349
      weighted avg       0.77      0.76      0.76      1349


Accuracy capturado en la ejecución 10: 75.69 [%]
F1-score capturado en la ejecución 10: 76.46 [%]

=== EJECUCIÓN 11 ===

--- TRAIN (ejecución 11) ---

--- TEST (ejecución 11) ---
2025-11-08 16:52:15.521151: 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`.
2025-11-08 16:52:15.532424: 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 00:00:1762617135.545553 1209958 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762617135.549745 1209958 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762617135.559509 1209958 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762617135.559527 1209958 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762617135.559529 1209958 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762617135.559537 1209958 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-08 16:52:15.562725: 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 00:00:1762617137.886121 1209958 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13760 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (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 1/17
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762617139.637304 1210093 service.cc:152] XLA service 0x77e96c0124f0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762617139.637335 1210093 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-08 16:52:19.674577: 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 00:00:1762617139.903856 1210093 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762617141.840921 1210093 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 2/17

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[1m 99/619[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6497 - loss: 0.7882
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Epoch 3/17

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Epoch 4/17

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Epoch 5/17

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[1m 31/619[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.8547 - loss: 0.4185  
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[1m 94/619[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.8330 - loss: 0.4497
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[1m192/619[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.8267 - loss: 0.4640
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Epoch 6/17

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Epoch 7/17

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[1m521/619[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.8733 - loss: 0.3765
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[1m581/619[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.8728 - loss: 0.3782
[1m615/619[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.8724 - loss: 0.3793
[1m619/619[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.8724 - loss: 0.3794 - val_accuracy: 0.7251 - val_loss: 0.7980
Epoch 8/17

[1m  1/619[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 19ms/step - accuracy: 0.8750 - loss: 0.4612
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[1m 64/619[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.9139 - loss: 0.3042
[1m 96/619[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.9135 - loss: 0.2988
[1m128/619[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.9111 - loss: 0.3010
[1m163/619[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.9096 - loss: 0.3014
[1m197/619[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.9078 - loss: 0.3041
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[1m354/619[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.9019 - loss: 0.3153
[1m388/619[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.9008 - loss: 0.3178
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[1m613/619[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.8961 - loss: 0.3275
[1m619/619[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.8960 - loss: 0.3277 - val_accuracy: 0.7219 - val_loss: 0.7868

[1m 1/43[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m39s[0m 948ms/step
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 21ms/step  
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 21ms/step
Saved model to disk.
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (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.
(1349, 6, 250)
(9904, 6, 250)

[1m  1/310[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5:51[0m 1s/step
[1m 51/310[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step 
[1m111/310[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 918us/step
[1m171/310[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 892us/step
[1m234/310[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 868us/step
[1m295/310[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 859us/step
[1m310/310[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step  
[1m310/310[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 4ms/step

[1m 1/43[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 15ms/step
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 18ms/step
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 18ms/step

[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m57/89[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 895us/step
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 998us/step
Global accuracy score (validation) = 72.09 [%]
Global F1 score (validation) = 72.47 [%]
[[0.]
 [0.]
 [0.]
 ...
 [3.]
 [3.]
 [3.]]
(1349, 1)
[[7.6731272e-02 8.3149177e-01 3.1695219e-03 8.8607408e-02]
 [8.7643772e-01 1.1987825e-01 1.1957060e-03 2.4883864e-03]
 [8.8657641e-01 9.6631005e-02 3.1089452e-03 1.3683660e-02]
 ...
 [4.8619974e-04 2.8330801e-04 1.0451766e-05 9.9922013e-01]
 [2.1627733e-04 6.1488565e-05 1.0561481e-05 9.9971169e-01]
 [6.6399164e-03 1.8430237e-02 9.6704149e-01 7.8883981e-03]]
(1349, 4)
-------------------------------------------------

Global accuracy score (train) = 86.18 [%]
Global accuracy score (test) = 71.61 [%]
Global F1 score (train) = 86.3 [%]
Global F1 score (test) = 72.74 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.55      0.55      0.55       350
MODERATE-INTENSITY       0.53      0.59      0.56       350
         SEDENTARY       0.98      0.90      0.93       350
VIGOROUS-INTENSITY       0.90      0.84      0.87       299

          accuracy                           0.72      1349
         macro avg       0.74      0.72      0.73      1349
      weighted avg       0.73      0.72      0.72      1349


Accuracy capturado en la ejecución 11: 71.61 [%]
F1-score capturado en la ejecución 11: 72.74 [%]

=== EJECUCIÓN 12 ===

--- TRAIN (ejecución 12) ---

--- TEST (ejecución 12) ---
2025-11-08 16:52:44.915196: 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`.
2025-11-08 16:52:44.926661: 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 00:00:1762617164.939844 1211682 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762617164.944234 1211682 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762617164.954585 1211682 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762617164.954606 1211682 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762617164.954608 1211682 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762617164.954610 1211682 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-08 16:52:44.957985: 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 00:00:1762617167.305364 1211682 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13760 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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Epoch 1/17
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762617169.061063 1211813 service.cc:152] XLA service 0x7e8538008610 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762617169.061107 1211813 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-08 16:52:49.098573: 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 00:00:1762617169.318989 1211813 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762617171.254112 1211813 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 2/17

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Epoch 3/17

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Epoch 4/17

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Epoch 5/17

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Epoch 6/17

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Epoch 7/17

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[1m 99/619[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.8838 - loss: 0.3172
[1m133/619[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.8802 - loss: 0.3285
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Epoch 8/17

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[1m133/619[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.8800 - loss: 0.3587
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[1m294/619[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.8816 - loss: 0.3550
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Epoch 9/17

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[1m 95/619[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.8915 - loss: 0.3097
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[1m 1/43[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m37s[0m 904ms/step
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Saved model to disk.
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Loaded model from disk.
(1349, 6, 250)
(9904, 6, 250)

[1m  1/310[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5:54[0m 1s/step
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[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m58/89[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 889us/step
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 982us/step
Global accuracy score (validation) = 72.96 [%]
Global F1 score (validation) = 73.44 [%]
[[0.]
 [0.]
 [0.]
 ...
 [3.]
 [3.]
 [3.]]
(1349, 1)
[[8.8012201e-01 1.1171361e-01 4.2784885e-03 3.8859639e-03]
 [6.3344514e-01 3.5865223e-01 4.2908667e-03 3.6117423e-03]
 [3.3210626e-01 6.6097069e-01 5.2159262e-04 6.4014797e-03]
 ...
 [6.8072611e-03 5.5865683e-03 4.9067510e-04 9.8711544e-01]
 [4.8024179e-03 3.8789061e-03 2.0106522e-04 9.9111772e-01]
 [8.4869592e-03 4.0555499e-02 9.1936338e-01 3.1594124e-02]]
(1349, 4)
-------------------------------------------------

Global accuracy score (train) = 86.05 [%]
Global accuracy score (test) = 74.13 [%]
Global F1 score (train) = 86.22 [%]
Global F1 score (test) = 74.58 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.61      0.50      0.55       350
MODERATE-INTENSITY       0.56      0.69      0.62       350
         SEDENTARY       0.93      0.95      0.94       350
VIGOROUS-INTENSITY       0.91      0.84      0.88       299

          accuracy                           0.74      1349
         macro avg       0.75      0.74      0.75      1349
      weighted avg       0.75      0.74      0.74      1349


Accuracy capturado en la ejecución 12: 74.13 [%]
F1-score capturado en la ejecución 12: 74.58 [%]

=== EJECUCIÓN 13 ===

--- TRAIN (ejecución 13) ---

--- TEST (ejecución 13) ---
2025-11-08 16:53:15.289100: 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`.
2025-11-08 16:53:15.300512: 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 00:00:1762617195.313586 1213497 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762617195.317781 1213497 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762617195.327685 1213497 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762617195.327704 1213497 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762617195.327706 1213497 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762617195.327708 1213497 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-08 16:53:15.330962: 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 00:00:1762617197.670189 1213497 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13758 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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Epoch 1/17
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762617199.435523 1213631 service.cc:152] XLA service 0x7e1f80001d40 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762617199.435555 1213631 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-08 16:53:19.470108: 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 00:00:1762617199.683924 1213631 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762617201.579717 1213631 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/619[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m33:15[0m 3s/step - accuracy: 0.4375 - loss: 1.5872
[1m 29/619[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.4744 - loss: 1.4003  
[1m 59/619[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5006 - loss: 1.3192
[1m 91/619[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5165 - loss: 1.2728
[1m122/619[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5233 - loss: 1.2438
[1m155/619[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5281 - loss: 1.2198
[1m186/619[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5329 - loss: 1.1995
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[1m283/619[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5442 - loss: 1.1532
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[1m485/619[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5616 - loss: 1.0872
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[1m551/619[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5662 - loss: 1.0700
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Epoch 2/17

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[1m360/619[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6586 - loss: 0.7461
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Epoch 3/17

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[1m 94/619[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.7046 - loss: 0.6413
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Epoch 4/17

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[1m298/619[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.7586 - loss: 0.5918
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Epoch 5/17

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[1m183/619[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.8075 - loss: 0.4775
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Epoch 6/17

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Epoch 7/17

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Saved model to disk.
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
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Loaded model from disk.
(1349, 6, 250)
(9904, 6, 250)

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[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 18ms/step
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 18ms/step

[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m55/89[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 926us/step
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step  
Global accuracy score (validation) = 72.05 [%]
Global F1 score (validation) = 72.87 [%]
[[0.]
 [0.]
 [0.]
 ...
 [3.]
 [3.]
 [3.]]
(1349, 1)
[[9.62852836e-01 3.64935957e-02 5.62208988e-05 5.97348204e-04]
 [2.41258834e-02 9.72283781e-01 2.60145869e-03 9.88850836e-04]
 [9.84805882e-01 1.43404594e-02 7.72369822e-05 7.76365807e-04]
 ...
 [4.03809012e-04 1.12719776e-03 9.50745307e-05 9.98373866e-01]
 [3.20359744e-04 8.00018897e-04 1.09738605e-04 9.98769939e-01]
 [3.47999064e-03 3.96555522e-03 9.85458255e-01 7.09620211e-03]]
(1349, 4)
-------------------------------------------------

Global accuracy score (train) = 86.63 [%]
Global accuracy score (test) = 72.2 [%]
Global F1 score (train) = 87.02 [%]
Global F1 score (test) = 73.48 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.54      0.62      0.58       350
MODERATE-INTENSITY       0.56      0.59      0.58       350
         SEDENTARY       0.97      0.88      0.92       350
VIGOROUS-INTENSITY       0.92      0.82      0.87       299

          accuracy                           0.72      1349
         macro avg       0.75      0.73      0.73      1349
      weighted avg       0.74      0.72      0.73      1349


Accuracy capturado en la ejecución 13: 72.2 [%]
F1-score capturado en la ejecución 13: 73.48 [%]

=== EJECUCIÓN 14 ===

--- TRAIN (ejecución 14) ---

--- TEST (ejecución 14) ---
2025-11-08 16:53:43.336239: 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`.
2025-11-08 16:53:43.348163: 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 00:00:1762617223.361495 1215124 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762617223.365464 1215124 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762617223.375513 1215124 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762617223.375533 1215124 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762617223.375535 1215124 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762617223.375536 1215124 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-08 16:53:43.378702: 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 00:00:1762617225.732549 1215124 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13758 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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Epoch 1/17
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762617227.537052 1215235 service.cc:152] XLA service 0x7f9b38007340 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762617227.537082 1215235 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-08 16:53:47.578986: 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 00:00:1762617227.804355 1215235 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762617229.728103 1215235 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 96/619[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5077 - loss: 1.2735
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[1m192/619[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5288 - loss: 1.1927
[1m223/619[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5325 - loss: 1.1772
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[1m384/619[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5475 - loss: 1.1193
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Epoch 2/17

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[1m 96/619[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6819 - loss: 0.7823
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Epoch 3/17

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[1m510/619[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.7028 - loss: 0.6878
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[1m572/619[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.7038 - loss: 0.6854
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Epoch 4/17

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[1m 31/619[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.7615 - loss: 0.5753  
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[1m 98/619[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.7587 - loss: 0.5799
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Epoch 5/17

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Epoch 6/17

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[1m 98/619[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.8285 - loss: 0.4693
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Epoch 7/17

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Epoch 8/17

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[1m 96/619[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.8873 - loss: 0.3522
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[1m192/619[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.8871 - loss: 0.3491
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[1m 1/43[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m38s[0m 926ms/step
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 21ms/step  
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Saved model to disk.
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Loaded model from disk.
(1349, 6, 250)
(9904, 6, 250)

[1m  1/310[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5:46[0m 1s/step
[1m 58/310[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 886us/step
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[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 17ms/step
[1m56/89[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 920us/step
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step  
Global accuracy score (validation) = 71.73 [%]
Global F1 score (validation) = 72.35 [%]
[[0.]
 [0.]
 [0.]
 ...
 [3.]
 [3.]
 [3.]]
(1349, 1)
[[9.4789520e-02 8.9464504e-01 2.8861859e-03 7.6792734e-03]
 [6.2292755e-01 3.7353042e-01 4.0971313e-04 3.1322718e-03]
 [7.1847051e-01 2.7403572e-01 2.7514127e-04 7.2186738e-03]
 ...
 [4.1548640e-04 2.3334622e-03 2.4997952e-04 9.9700111e-01]
 [3.5626965e-04 4.9146016e-05 2.1357034e-06 9.9959242e-01]
 [2.3862837e-02 2.8843883e-02 3.0125692e-02 9.1716766e-01]]
(1349, 4)
-------------------------------------------------

Global accuracy score (train) = 84.94 [%]
Global accuracy score (test) = 74.05 [%]
Global F1 score (train) = 85.43 [%]
Global F1 score (test) = 74.95 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.59      0.54      0.57       350
MODERATE-INTENSITY       0.55      0.65      0.59       350
         SEDENTARY       0.98      0.93      0.95       350
VIGOROUS-INTENSITY       0.91      0.87      0.89       299

          accuracy                           0.74      1349
         macro avg       0.76      0.75      0.75      1349
      weighted avg       0.75      0.74      0.74      1349


Accuracy capturado en la ejecución 14: 74.05 [%]
F1-score capturado en la ejecución 14: 74.95 [%]

=== EJECUCIÓN 15 ===

--- TRAIN (ejecución 15) ---

--- TEST (ejecución 15) ---
2025-11-08 16:54:12.686634: 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`.
2025-11-08 16:54:12.698382: 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 00:00:1762617252.712642 1216825 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762617252.716891 1216825 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762617252.727705 1216825 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762617252.727723 1216825 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762617252.727725 1216825 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762617252.727727 1216825 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-08 16:54:12.730888: 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 00:00:1762617255.044680 1216825 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13762 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 1/17
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762617256.791775 1216956 service.cc:152] XLA service 0x7e0300010540 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762617256.791828 1216956 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-08 16:54:16.834977: 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 00:00:1762617257.064751 1216956 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762617258.963471 1216956 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/619[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m33:31[0m 3s/step - accuracy: 0.2500 - loss: 1.8706
[1m 28/619[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.4355 - loss: 1.3265  
[1m 62/619[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4647 - loss: 1.2706
[1m 95/619[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4772 - loss: 1.2442
[1m125/619[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4854 - loss: 1.2247
[1m157/619[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4931 - loss: 1.2049
[1m192/619[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5010 - loss: 1.1855
[1m224/619[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5072 - loss: 1.1700
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Epoch 2/17

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Epoch 3/17

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[1m 95/619[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.7422 - loss: 0.5974
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Epoch 4/17

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Epoch 5/17

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Epoch 6/17

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Epoch 7/17

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[1m299/619[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.8489 - loss: 0.4239
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Saved model to disk.
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
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Loaded model from disk.
(1349, 6, 250)
(9904, 6, 250)

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[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 18ms/step
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 18ms/step

[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m50/89[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step 
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step
Global accuracy score (validation) = 66.71 [%]
Global F1 score (validation) = 67.26 [%]
[[0.]
 [0.]
 [0.]
 ...
 [3.]
 [3.]
 [3.]]
(1349, 1)
[[9.0277082e-01 8.6841330e-02 4.6614646e-03 5.7263551e-03]
 [9.3690079e-01 6.2401880e-02 4.4896948e-05 6.5243023e-04]
 [6.6246098e-01 3.3300105e-01 3.6358606e-04 4.1743745e-03]
 ...
 [1.2974133e-03 7.1524479e-04 2.3698792e-04 9.9775034e-01]
 [2.6684185e-03 3.0192740e-03 1.2336606e-03 9.9307865e-01]
 [2.9272040e-02 5.7963047e-02 7.6572490e-01 1.4703996e-01]]
(1349, 4)
-------------------------------------------------

Global accuracy score (train) = 82.93 [%]
Global accuracy score (test) = 71.09 [%]
Global F1 score (train) = 82.99 [%]
Global F1 score (test) = 71.58 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.65      0.52      0.58       350
MODERATE-INTENSITY       0.57      0.74      0.64       350
         SEDENTARY       0.98      0.76      0.85       350
VIGOROUS-INTENSITY       0.74      0.85      0.79       299

          accuracy                           0.71      1349
         macro avg       0.73      0.72      0.72      1349
      weighted avg       0.73      0.71      0.71      1349


Accuracy capturado en la ejecución 15: 71.09 [%]
F1-score capturado en la ejecución 15: 71.58 [%]

=== EJECUCIÓN 16 ===

--- TRAIN (ejecución 16) ---

--- TEST (ejecución 16) ---
2025-11-08 16:54:40.684194: 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`.
2025-11-08 16:54:40.696140: 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 00:00:1762617280.710011 1218463 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762617280.714327 1218463 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762617280.724193 1218463 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762617280.724214 1218463 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762617280.724216 1218463 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762617280.724218 1218463 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-08 16:54:40.727400: 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 00:00:1762617283.040049 1218463 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13760 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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Epoch 1/17
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762617284.787810 1218590 service.cc:152] XLA service 0x77a778012fb0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762617284.787840 1218590 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-08 16:54:44.822398: 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 00:00:1762617285.037449 1218590 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762617286.989171 1218590 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 96/619[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4817 - loss: 1.2619
[1m128/619[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4962 - loss: 1.2250
[1m161/619[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5081 - loss: 1.1950
[1m196/619[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5174 - loss: 1.1712
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Epoch 2/17

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Epoch 3/17

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[1m299/619[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.7053 - loss: 0.6574
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Epoch 4/17

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[1m 31/619[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.7528 - loss: 0.5919  
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[1m 96/619[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.7531 - loss: 0.5903
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Epoch 5/17

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[1m299/619[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.7999 - loss: 0.5129
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Epoch 6/17

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[1m101/619[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.8277 - loss: 0.4769
[1m133/619[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.8270 - loss: 0.4790
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[1m201/619[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.8267 - loss: 0.4782
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Epoch 7/17

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[1m 1/43[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m38s[0m 926ms/step
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 20ms/step  
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 21ms/step
Saved model to disk.
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Loaded model from disk.
(1349, 6, 250)
(9904, 6, 250)

[1m  1/310[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5:36[0m 1s/step
[1m 58/310[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 891us/step
[1m118/310[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 866us/step
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[1m 1/43[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 21ms/step
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 18ms/step
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 18ms/step

[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m61/89[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 837us/step
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 976us/step
Global accuracy score (validation) = 72.05 [%]
Global F1 score (validation) = 72.59 [%]
[[0.]
 [0.]
 [0.]
 ...
 [3.]
 [3.]
 [3.]]
(1349, 1)
[[7.53199160e-01 2.35750750e-01 5.59127145e-03 5.45883179e-03]
 [9.09961939e-01 7.76073039e-02 7.59734819e-03 4.83337510e-03]
 [8.72773349e-01 1.15928374e-01 6.77840551e-03 4.51986352e-03]
 ...
 [6.00601314e-04 3.51985334e-04 1.57004150e-04 9.98890460e-01]
 [1.32167351e-03 1.21721474e-03 1.05454586e-03 9.96406496e-01]
 [2.50118673e-02 2.47375052e-02 9.09289837e-01 4.09607664e-02]]
(1349, 4)
-------------------------------------------------

Global accuracy score (train) = 84.47 [%]
Global accuracy score (test) = 75.76 [%]
Global F1 score (train) = 84.7 [%]
Global F1 score (test) = 76.39 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.62      0.52      0.57       350
MODERATE-INTENSITY       0.56      0.69      0.62       350
         SEDENTARY       0.97      0.97      0.97       350
VIGOROUS-INTENSITY       0.93      0.86      0.90       299

          accuracy                           0.76      1349
         macro avg       0.77      0.76      0.76      1349
      weighted avg       0.77      0.76      0.76      1349


Accuracy capturado en la ejecución 16: 75.76 [%]
F1-score capturado en la ejecución 16: 76.39 [%]

=== EJECUCIÓN 17 ===

--- TRAIN (ejecución 17) ---

--- TEST (ejecución 17) ---
2025-11-08 16:55:08.528921: 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`.
2025-11-08 16:55:08.540504: 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 00:00:1762617308.553789 1220060 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762617308.557941 1220060 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762617308.567642 1220060 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762617308.567660 1220060 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762617308.567662 1220060 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762617308.567663 1220060 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-08 16:55:08.570797: 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 00:00:1762617310.879217 1220060 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13760 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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Epoch 1/17
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762617312.655562 1220199 service.cc:152] XLA service 0x7bc278012470 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762617312.655611 1220199 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-08 16:55:12.691623: 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 00:00:1762617312.918725 1220199 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762617314.852444 1220199 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 2/17

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Epoch 3/17

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Epoch 4/17

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[1m585/619[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.7713 - loss: 0.5668
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Epoch 5/17

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Epoch 6/17

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Epoch 7/17

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Epoch 8/17

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[1m 1/43[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m39s[0m 931ms/step
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 21ms/step  
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 21ms/step
Saved model to disk.
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (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.
(1349, 6, 250)
(9904, 6, 250)

[1m  1/310[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5:41[0m 1s/step
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[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step  
Global accuracy score (validation) = 72.26 [%]
Global F1 score (validation) = 72.7 [%]
[[0.]
 [0.]
 [0.]
 ...
 [3.]
 [3.]
 [3.]]
(1349, 1)
[[8.0222422e-01 1.5206951e-01 4.1562721e-02 4.1436525e-03]
 [7.2075915e-01 2.1615869e-01 5.0520934e-02 1.2561202e-02]
 [8.4284925e-01 1.3715196e-01 1.7434021e-02 2.5647634e-03]
 ...
 [3.6755402e-03 2.3245553e-03 4.5439720e-04 9.9354559e-01]
 [6.6560949e-03 6.6412324e-03 9.6054340e-04 9.8574215e-01]
 [5.7276078e-03 9.5369089e-03 9.2182857e-01 6.2906846e-02]]
(1349, 4)
-------------------------------------------------

Global accuracy score (train) = 84.33 [%]
Global accuracy score (test) = 76.43 [%]
Global F1 score (train) = 84.41 [%]
Global F1 score (test) = 77.09 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.60      0.65      0.62       350
MODERATE-INTENSITY       0.60      0.58      0.59       350
         SEDENTARY       0.97      0.98      0.97       350
VIGOROUS-INTENSITY       0.94      0.87      0.90       299

          accuracy                           0.76      1349
         macro avg       0.78      0.77      0.77      1349
      weighted avg       0.77      0.76      0.77      1349


Accuracy capturado en la ejecución 17: 76.43 [%]
F1-score capturado en la ejecución 17: 77.09 [%]

=== EJECUCIÓN 18 ===

--- TRAIN (ejecución 18) ---

--- TEST (ejecución 18) ---
2025-11-08 16:55:37.627161: 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`.
2025-11-08 16:55:37.638514: 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 00:00:1762617337.651797 1221783 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762617337.655967 1221783 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762617337.666008 1221783 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762617337.666030 1221783 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762617337.666033 1221783 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762617337.666034 1221783 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-08 16:55:37.669207: 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 00:00:1762617339.985950 1221783 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13760 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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Epoch 1/17
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762617341.762767 1221913 service.cc:152] XLA service 0x7b7240022a50 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762617341.762802 1221913 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-08 16:55:41.797694: 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 00:00:1762617342.012776 1221913 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762617343.963736 1221913 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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[1m195/619[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5108 - loss: 1.1638
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Epoch 2/17

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Epoch 3/17

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Epoch 4/17

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[1m 31/619[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.7463 - loss: 0.5988  
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[1m 99/619[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.7610 - loss: 0.5746
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[1m199/619[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.7641 - loss: 0.5734
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[1m298/619[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.7646 - loss: 0.5737
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Epoch 5/17

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Epoch 6/17

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[1m 95/619[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.8325 - loss: 0.4963
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Epoch 7/17

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[1m 1/43[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m40s[0m 953ms/step
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 20ms/step  
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Saved model to disk.
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Loaded model from disk.
(1349, 6, 250)
(9904, 6, 250)

[1m  1/310[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5:42[0m 1s/step
[1m 51/310[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step 
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[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 15ms/step
[1m58/89[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 883us/step
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Global accuracy score (validation) = 71.52 [%]
Global F1 score (validation) = 72.18 [%]
[[0.]
 [0.]
 [0.]
 ...
 [3.]
 [3.]
 [3.]]
(1349, 1)
[[5.50249852e-02 9.38929379e-01 1.04745082e-03 4.99821408e-03]
 [8.27381015e-01 1.62496924e-01 7.28549529e-03 2.83652404e-03]
 [1.72520667e-01 8.17365229e-01 2.83096544e-03 7.28320703e-03]
 ...
 [3.66017758e-03 5.79152396e-03 2.13738298e-04 9.90334570e-01]
 [1.24409242e-04 1.92098450e-05 5.34278661e-06 9.99850988e-01]
 [2.94713350e-03 1.26542365e-02 9.77234900e-01 7.16378540e-03]]
(1349, 4)
-------------------------------------------------

Global accuracy score (train) = 85.45 [%]
Global accuracy score (test) = 75.98 [%]
Global F1 score (train) = 85.69 [%]
Global F1 score (test) = 76.78 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.60      0.67      0.63       350
MODERATE-INTENSITY       0.59      0.59      0.59       350
         SEDENTARY       0.98      0.97      0.98       350
VIGOROUS-INTENSITY       0.94      0.81      0.87       299

          accuracy                           0.76      1349
         macro avg       0.78      0.76      0.77      1349
      weighted avg       0.77      0.76      0.76      1349


Accuracy capturado en la ejecución 18: 75.98 [%]
F1-score capturado en la ejecución 18: 76.78 [%]

=== EJECUCIÓN 19 ===

--- TRAIN (ejecución 19) ---

--- TEST (ejecución 19) ---
2025-11-08 16:56:05.803969: 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`.
2025-11-08 16:56:05.815237: 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 00:00:1762617365.828171 1223407 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762617365.832098 1223407 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762617365.842135 1223407 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762617365.842154 1223407 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762617365.842156 1223407 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762617365.842158 1223407 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-08 16:56:05.845327: 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 00:00:1762617368.190672 1223407 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13760 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (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 1/17
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762617369.953720 1223517 service.cc:152] XLA service 0x75239c005f50 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762617369.953758 1223517 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-08 16:56:09.988836: 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 00:00:1762617370.204399 1223517 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762617372.103738 1223517 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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[1m368/619[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5325 - loss: 1.1196
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[1m503/619[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5451 - loss: 1.0807
[1m536/619[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5480 - loss: 1.0724
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[1m619/619[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m5s[0m 3ms/step - accuracy: 0.5547 - loss: 1.0534 - val_accuracy: 0.4417 - val_loss: 1.2088
Epoch 2/17

[1m  1/619[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 20ms/step - accuracy: 0.6250 - loss: 0.7700
[1m 34/619[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6620 - loss: 0.7914  
[1m 67/619[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6591 - loss: 0.7852
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[1m199/619[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6644 - loss: 0.7675
[1m234/619[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6650 - loss: 0.7661
[1m267/619[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6654 - loss: 0.7645
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Epoch 3/17

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Epoch 4/17

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[1m299/619[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.7545 - loss: 0.5653
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Epoch 5/17

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[1m 96/619[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.7838 - loss: 0.4872
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Epoch 6/17

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Epoch 7/17

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[1m 98/619[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.8621 - loss: 0.4148
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[1m 1/43[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m39s[0m 945ms/step
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Saved model to disk.
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Loaded model from disk.
(1349, 6, 250)
(9904, 6, 250)

[1m  1/310[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5:43[0m 1s/step
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[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m55/89[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 928us/step
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step  
Global accuracy score (validation) = 71.91 [%]
Global F1 score (validation) = 72.25 [%]
[[0.]
 [0.]
 [0.]
 ...
 [3.]
 [3.]
 [3.]]
(1349, 1)
[[2.69509315e-01 1.54297575e-01 2.84693334e-02 5.47723830e-01]
 [7.09587216e-01 2.56980091e-01 1.95185672e-02 1.39141325e-02]
 [1.60062820e-01 8.14991593e-01 1.59752592e-02 8.97041056e-03]
 ...
 [1.96358960e-04 8.70847507e-05 5.19564128e-05 9.99664545e-01]
 [6.74732670e-04 3.41241859e-04 2.25298587e-04 9.98758793e-01]
 [8.95828102e-03 8.65487754e-03 6.78826928e-01 3.03559929e-01]]
(1349, 4)
-------------------------------------------------

Global accuracy score (train) = 85.02 [%]
Global accuracy score (test) = 75.61 [%]
Global F1 score (train) = 85.07 [%]
Global F1 score (test) = 75.94 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.66      0.51      0.58       350
MODERATE-INTENSITY       0.57      0.70      0.63       350
         SEDENTARY       0.97      0.97      0.97       350
VIGOROUS-INTENSITY       0.88      0.85      0.86       299

          accuracy                           0.76      1349
         macro avg       0.77      0.76      0.76      1349
      weighted avg       0.76      0.76      0.76      1349


Accuracy capturado en la ejecución 19: 75.61 [%]
F1-score capturado en la ejecución 19: 75.94 [%]

=== EJECUCIÓN 20 ===

--- TRAIN (ejecución 20) ---

--- TEST (ejecución 20) ---
2025-11-08 16:56:33.750160: 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`.
2025-11-08 16:56:33.761677: 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 00:00:1762617393.775772 1225012 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762617393.779918 1225012 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762617393.789953 1225012 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762617393.789973 1225012 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762617393.789975 1225012 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762617393.789977 1225012 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-08 16:56:33.793008: 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 00:00:1762617396.124632 1225012 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13760 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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Epoch 1/17
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762617397.866124 1225139 service.cc:152] XLA service 0x7cbc240139e0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762617397.866154 1225139 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-08 16:56:37.900817: 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 00:00:1762617398.116085 1225139 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762617400.017662 1225139 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/619[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m33:20[0m 3s/step - accuracy: 0.0625 - loss: 2.3486
[1m 27/619[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.3546 - loss: 1.6420  
[1m 60/619[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4245 - loss: 1.4659
[1m 92/619[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4530 - loss: 1.3847
[1m125/619[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4732 - loss: 1.3259
[1m155/619[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4851 - loss: 1.2896
[1m189/619[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4943 - loss: 1.2583
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Epoch 2/17

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[1m298/619[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6760 - loss: 0.7386
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Epoch 3/17

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[1m101/619[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.7091 - loss: 0.6659
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[1m201/619[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.7204 - loss: 0.6508
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Epoch 4/17

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Epoch 5/17

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[1m 31/619[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.8320 - loss: 0.5167  
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[1m231/619[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.8111 - loss: 0.5190
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[1m298/619[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.8078 - loss: 0.5221
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Epoch 6/17

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[1m 59/619[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.8544 - loss: 0.4621
[1m 90/619[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.8503 - loss: 0.4660
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Epoch 7/17

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[1m 99/619[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.8683 - loss: 0.4082
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Epoch 8/17

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[1m 99/619[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.8996 - loss: 0.2781
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[1m232/619[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.8911 - loss: 0.3114
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Epoch 9/17

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[1m201/619[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.8955 - loss: 0.3280
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Epoch 10/17

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[1m 31/619[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.9202 - loss: 0.2708  
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[1m 99/619[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.9084 - loss: 0.3057
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[1m168/619[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.9081 - loss: 0.3068
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[1m299/619[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.9084 - loss: 0.3058
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[1m 1/43[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m38s[0m 908ms/step
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Saved model to disk.
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Loaded model from disk.
(1349, 6, 250)
(9904, 6, 250)

[1m  1/310[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5:47[0m 1s/step
[1m 56/310[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 916us/step
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[1m172/310[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 883us/step
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[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m57/89[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 905us/step
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step  
Global accuracy score (validation) = 72.23 [%]
Global F1 score (validation) = 72.21 [%]
[[0.]
 [0.]
 [0.]
 ...
 [3.]
 [3.]
 [3.]]
(1349, 1)
[[8.9854324e-01 7.0573717e-02 2.5586339e-02 5.2966722e-03]
 [5.8168417e-01 2.1714294e-01 1.9050449e-01 1.0668456e-02]
 [6.9370180e-01 2.7675220e-01 2.1226048e-02 8.3199926e-03]
 ...
 [2.1062477e-03 2.4162102e-03 4.1044480e-04 9.9506712e-01]
 [9.3458354e-04 5.0663529e-04 1.3013469e-04 9.9842852e-01]
 [5.6350231e-03 6.8028066e-03 9.7933185e-01 8.2302699e-03]]
(1349, 4)
-------------------------------------------------

Global accuracy score (train) = 83.71 [%]
Global accuracy score (test) = 74.5 [%]
Global F1 score (train) = 83.69 [%]
Global F1 score (test) = 74.64 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.63      0.44      0.52       350
MODERATE-INTENSITY       0.55      0.71      0.62       350
         SEDENTARY       0.92      0.97      0.94       350
VIGOROUS-INTENSITY       0.94      0.87      0.90       299

          accuracy                           0.74      1349
         macro avg       0.76      0.75      0.75      1349
      weighted avg       0.75      0.74      0.74      1349


Accuracy capturado en la ejecución 20: 74.5 [%]
F1-score capturado en la ejecución 20: 74.64 [%]

=== EJECUCIÓN 21 ===

--- TRAIN (ejecución 21) ---

--- TEST (ejecución 21) ---
2025-11-08 16:57:05.421615: 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`.
2025-11-08 16:57:05.432943: 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 00:00:1762617425.446128 1226919 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762617425.450166 1226919 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762617425.460208 1226919 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762617425.460233 1226919 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762617425.460235 1226919 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762617425.460237 1226919 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-08 16:57:05.463251: 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 00:00:1762617427.788866 1226919 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13760 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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Epoch 1/17
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762617429.566092 1227055 service.cc:152] XLA service 0x761a500049e0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762617429.566141 1227055 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-08 16:57:09.604949: 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 00:00:1762617429.827906 1227055 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762617431.773981 1227055 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/619[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m33:58[0m 3s/step - accuracy: 0.1875 - loss: 2.1583
[1m 30/619[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.4511 - loss: 1.4326  
[1m 63/619[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4966 - loss: 1.3118
[1m 97/619[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5185 - loss: 1.2487
[1m129/619[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5279 - loss: 1.2131
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[1m194/619[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5391 - loss: 1.1681
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Epoch 2/17

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Epoch 3/17

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[1m 99/619[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.7225 - loss: 0.6597
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Epoch 4/17

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Epoch 5/17

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[1m 31/619[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.8419 - loss: 0.4768  
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[1m 94/619[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.8339 - loss: 0.4899
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[1m192/619[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.8237 - loss: 0.5042
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[1m294/619[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.8213 - loss: 0.5038
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Epoch 6/17

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Epoch 7/17

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Epoch 8/17

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[1m 1/43[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m38s[0m 925ms/step
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 19ms/step  
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Saved model to disk.
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Loaded model from disk.
(1349, 6, 250)
(9904, 6, 250)

[1m  1/310[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5:42[0m 1s/step
[1m 50/310[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step 
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[1m159/310[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 951us/step
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[1m 1/43[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 16ms/step
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 18ms/step
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 19ms/step

[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m53/89[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 977us/step
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step  
Global accuracy score (validation) = 70.61 [%]
Global F1 score (validation) = 70.43 [%]
[[0.]
 [0.]
 [0.]
 ...
 [3.]
 [3.]
 [3.]]
(1349, 1)
[[5.3155106e-02 8.8621384e-01 1.2617928e-02 4.8013031e-02]
 [1.4530285e-01 8.4672099e-01 7.3802116e-04 7.2381212e-03]
 [9.6310711e-01 2.7373806e-02 6.1634788e-03 3.3555876e-03]
 ...
 [4.2522128e-04 1.6694216e-04 1.0720890e-04 9.9930072e-01]
 [5.4693097e-05 1.4067449e-05 1.4141526e-05 9.9991715e-01]
 [1.6883766e-02 3.7559953e-02 9.0932316e-01 3.6233064e-02]]
(1349, 4)
-------------------------------------------------

Global accuracy score (train) = 86.3 [%]
Global accuracy score (test) = 73.39 [%]
Global F1 score (train) = 86.24 [%]
Global F1 score (test) = 73.41 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.60      0.53      0.57       350
MODERATE-INTENSITY       0.55      0.57      0.56       350
         SEDENTARY       0.96      0.98      0.97       350
VIGOROUS-INTENSITY       0.81      0.88      0.84       299

          accuracy                           0.73      1349
         macro avg       0.73      0.74      0.73      1349
      weighted avg       0.73      0.73      0.73      1349


Accuracy capturado en la ejecución 21: 73.39 [%]
F1-score capturado en la ejecución 21: 73.41 [%]

=== EJECUCIÓN 22 ===

--- TRAIN (ejecución 22) ---

--- TEST (ejecución 22) ---
2025-11-08 16:57:34.600137: 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`.
2025-11-08 16:57:34.611578: 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 00:00:1762617454.625012 1228650 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762617454.628996 1228650 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762617454.638953 1228650 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762617454.638974 1228650 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762617454.638978 1228650 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762617454.638980 1228650 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-08 16:57:34.642204: 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 00:00:1762617456.963266 1228650 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13760 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (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 1/17
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762617458.710096 1228780 service.cc:152] XLA service 0x776490002f50 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762617458.710147 1228780 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-08 16:57:38.744919: 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 00:00:1762617458.962358 1228780 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762617460.891239 1228780 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 2/17

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[1m 98/619[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6599 - loss: 0.7944
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Epoch 3/17

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Epoch 4/17

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[1m231/619[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.7751 - loss: 0.5574
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[1m294/619[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.7730 - loss: 0.5586
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Epoch 5/17

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[1m 31/619[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.8376 - loss: 0.4614  
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Epoch 6/17

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Epoch 7/17

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[1m 97/619[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.8642 - loss: 0.3925
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[1m199/619[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.8695 - loss: 0.3855
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Epoch 8/17

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[1m 1/43[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m38s[0m 928ms/step
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 20ms/step  
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 21ms/step
Saved model to disk.
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Loaded model from disk.
(1349, 6, 250)
(9904, 6, 250)

[1m  1/310[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5:46[0m 1s/step
[1m 57/310[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 904us/step
[1m120/310[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 847us/step
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[1m241/310[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 839us/step
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[1m 1/43[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 17ms/step
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 18ms/step
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 18ms/step

[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m59/89[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 870us/step
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step  
Global accuracy score (validation) = 73.63 [%]
Global F1 score (validation) = 74.09 [%]
[[0.]
 [0.]
 [0.]
 ...
 [3.]
 [3.]
 [3.]]
(1349, 1)
[[8.9130270e-01 1.0254661e-01 2.6548535e-03 3.4958781e-03]
 [8.7398368e-01 1.1938412e-01 2.8801206e-03 3.7520966e-03]
 [8.1289041e-01 1.7908907e-01 3.2179256e-03 4.8026671e-03]
 ...
 [2.1197742e-03 1.7297581e-03 3.3610608e-04 9.9581438e-01]
 [4.5465524e-03 1.4447474e-03 1.0692109e-04 9.9390167e-01]
 [3.0010599e-03 5.6351610e-03 9.5367575e-01 3.7688043e-02]]
(1349, 4)
-------------------------------------------------

Global accuracy score (train) = 86.12 [%]
Global accuracy score (test) = 73.76 [%]
Global F1 score (train) = 86.24 [%]
Global F1 score (test) = 74.31 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.60      0.48      0.53       350
MODERATE-INTENSITY       0.54      0.69      0.60       350
         SEDENTARY       0.98      0.96      0.97       350
VIGOROUS-INTENSITY       0.89      0.84      0.86       299

          accuracy                           0.74      1349
         macro avg       0.75      0.74      0.74      1349
      weighted avg       0.75      0.74      0.74      1349


Accuracy capturado en la ejecución 22: 73.76 [%]
F1-score capturado en la ejecución 22: 74.31 [%]

=== EJECUCIÓN 23 ===

--- TRAIN (ejecución 23) ---

--- TEST (ejecución 23) ---
2025-11-08 16:58:03.762541: 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`.
2025-11-08 16:58:03.773636: 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 00:00:1762617483.786826 1230364 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762617483.790983 1230364 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762617483.801262 1230364 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762617483.801283 1230364 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762617483.801286 1230364 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762617483.801288 1230364 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-08 16:58:03.804530: 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 00:00:1762617486.155587 1230364 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13760 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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Epoch 1/17
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762617487.922085 1230499 service.cc:152] XLA service 0x7db444006ca0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762617487.922118 1230499 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-08 16:58:07.958423: 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 00:00:1762617488.184766 1230499 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762617490.082481 1230499 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 2/17

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Epoch 3/17

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Epoch 4/17

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[1m 99/619[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.7932 - loss: 0.5393
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[1m230/619[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.7729 - loss: 0.5629
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Epoch 5/17

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Epoch 6/17

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Epoch 7/17

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[1m 31/619[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.8472 - loss: 0.3613  
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Epoch 8/17

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[1m 1/43[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m38s[0m 922ms/step
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 20ms/step  
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Saved model to disk.
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (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.
(1349, 6, 250)
(9904, 6, 250)

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[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m50/89[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step 
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step
Global accuracy score (validation) = 72.23 [%]
Global F1 score (validation) = 72.24 [%]
[[0.]
 [0.]
 [0.]
 ...
 [3.]
 [3.]
 [3.]]
(1349, 1)
[[3.37340981e-01 5.86785257e-01 3.44241187e-02 4.14496288e-02]
 [9.04772222e-01 8.63266215e-02 3.87492101e-03 5.02630835e-03]
 [6.61066324e-02 7.35392511e-01 4.10274416e-03 1.94398135e-01]
 ...
 [1.93049235e-03 1.01307547e-03 4.35067661e-04 9.96621370e-01]
 [2.28076140e-04 2.72076024e-04 1.02589736e-04 9.99397278e-01]
 [2.87162494e-02 3.72321270e-02 7.84173846e-01 1.49877772e-01]]
(1349, 4)
-------------------------------------------------

Global accuracy score (train) = 84.06 [%]
Global accuracy score (test) = 72.5 [%]
Global F1 score (train) = 84.18 [%]
Global F1 score (test) = 72.22 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.62      0.35      0.45       350
MODERATE-INTENSITY       0.52      0.74      0.61       350
         SEDENTARY       0.97      0.95      0.96       350
VIGOROUS-INTENSITY       0.86      0.88      0.87       299

          accuracy                           0.72      1349
         macro avg       0.74      0.73      0.72      1349
      weighted avg       0.74      0.72      0.72      1349


Accuracy capturado en la ejecución 23: 72.5 [%]
F1-score capturado en la ejecución 23: 72.22 [%]

=== EJECUCIÓN 24 ===

--- TRAIN (ejecución 24) ---

--- TEST (ejecución 24) ---
2025-11-08 16:58:33.038960: 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`.
2025-11-08 16:58:33.050762: 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 00:00:1762617513.064089 1232093 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762617513.068193 1232093 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762617513.078114 1232093 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762617513.078132 1232093 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762617513.078134 1232093 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762617513.078135 1232093 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-08 16:58:33.081300: 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 00:00:1762617515.404988 1232093 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13758 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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Epoch 1/17
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762617517.199059 1232204 service.cc:152] XLA service 0x70e59c011810 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762617517.199101 1232204 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-08 16:58:37.235073: 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 00:00:1762617517.458880 1232204 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762617519.377357 1232204 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/619[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m33:48[0m 3s/step - accuracy: 0.5000 - loss: 1.6420
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[1m 90/619[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4739 - loss: 1.3457
[1m125/619[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4882 - loss: 1.2924
[1m158/619[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4988 - loss: 1.2567
[1m191/619[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5082 - loss: 1.2281
[1m225/619[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5154 - loss: 1.2050
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[1m293/619[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5261 - loss: 1.1683
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[1m396/619[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5390 - loss: 1.1266
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[1m495/619[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5493 - loss: 1.0951
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Epoch 2/17

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Epoch 3/17

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[1m485/619[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.7032 - loss: 0.6642
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Epoch 4/17

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[1m 31/619[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.7618 - loss: 0.5703  
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[1m 95/619[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.7528 - loss: 0.6015
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Epoch 5/17

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Epoch 6/17

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[1m 98/619[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.8358 - loss: 0.4160
[1m133/619[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.8376 - loss: 0.4199
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[1m201/619[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.8391 - loss: 0.4235
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Epoch 7/17

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[1m 1/43[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m39s[0m 939ms/step
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 20ms/step  
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Saved model to disk.
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Loaded model from disk.
(1349, 6, 250)
(9904, 6, 250)

[1m  1/310[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5:41[0m 1s/step
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[1m61/89[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 837us/step
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 972us/step
Global accuracy score (validation) = 71.63 [%]
Global F1 score (validation) = 72.22 [%]
[[0.]
 [0.]
 [0.]
 ...
 [3.]
 [3.]
 [3.]]
(1349, 1)
[[9.8308510e-01 1.6484329e-02 2.3407850e-04 1.9657698e-04]
 [9.2943561e-01 5.7831593e-02 7.1165040e-03 5.6163189e-03]
 [9.3823856e-01 4.9815789e-02 6.8789264e-03 5.0666933e-03]
 ...
 [1.0366279e-02 2.4591118e-02 1.1037504e-03 9.6393889e-01]
 [3.3684832e-03 1.7099188e-03 6.8138476e-04 9.9424011e-01]
 [3.9447070e-04 2.1165753e-04 9.9919325e-01 2.0063158e-04]]
(1349, 4)
-------------------------------------------------

Global accuracy score (train) = 85.97 [%]
Global accuracy score (test) = 72.05 [%]
Global F1 score (train) = 86.16 [%]
Global F1 score (test) = 72.91 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.54      0.56      0.55       350
MODERATE-INTENSITY       0.51      0.54      0.53       350
         SEDENTARY       0.97      0.97      0.97       350
VIGOROUS-INTENSITY       0.91      0.83      0.87       299

          accuracy                           0.72      1349
         macro avg       0.73      0.72      0.73      1349
      weighted avg       0.73      0.72      0.72      1349


Accuracy capturado en la ejecución 24: 72.05 [%]
F1-score capturado en la ejecución 24: 72.91 [%]

=== EJECUCIÓN 25 ===

--- TRAIN (ejecución 25) ---

--- TEST (ejecución 25) ---
2025-11-08 16:59:01.014694: 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`.
2025-11-08 16:59:01.025966: 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 00:00:1762617541.039059 1233689 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762617541.043183 1233689 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762617541.053127 1233689 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762617541.053145 1233689 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762617541.053148 1233689 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762617541.053150 1233689 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-08 16:59:01.056505: 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 00:00:1762617543.394970 1233689 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13758 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (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 1/17
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762617545.200230 1233829 service.cc:152] XLA service 0x75a3e0010290 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762617545.200261 1233829 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-08 16:59:05.238722: 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 00:00:1762617545.464554 1233829 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762617547.389991 1233829 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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[1m365/619[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5202 - loss: 1.1475
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[1m497/619[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5378 - loss: 1.0995
[1m529/619[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5411 - loss: 1.0901
[1m563/619[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5444 - loss: 1.0807
[1m595/619[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.5475 - loss: 1.0724
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Epoch 2/17

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[1m 33/619[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.7044 - loss: 0.7407  
[1m 68/619[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.7045 - loss: 0.7345
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[1m132/619[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6998 - loss: 0.7277
[1m166/619[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6953 - loss: 0.7298
[1m197/619[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6923 - loss: 0.7306
[1m228/619[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6905 - loss: 0.7306
[1m261/619[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6892 - loss: 0.7301
[1m296/619[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6876 - loss: 0.7304
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Epoch 3/17

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Epoch 4/17

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[1m 99/619[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.7429 - loss: 0.5871
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[1m299/619[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.7421 - loss: 0.5881
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[1m360/619[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.7426 - loss: 0.5868
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[1m584/619[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.7443 - loss: 0.5823
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Epoch 5/17

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[1m 98/619[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.8028 - loss: 0.4884
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Epoch 6/17

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Epoch 7/17

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[1m 97/619[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.8102 - loss: 0.4376
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[1m606/619[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.8260 - loss: 0.4376
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Epoch 8/17

[1m  1/619[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 19ms/step - accuracy: 0.8125 - loss: 0.5435
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[1m 64/619[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.8547 - loss: 0.3848
[1m 97/619[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.8527 - loss: 0.3932
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[1m389/619[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.8519 - loss: 0.4048
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[1m585/619[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.8523 - loss: 0.4054
[1m619/619[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.8523 - loss: 0.4056
[1m619/619[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.8523 - loss: 0.4056 - val_accuracy: 0.7202 - val_loss: 0.8031

[1m 1/43[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m39s[0m 940ms/step
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 20ms/step  
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 21ms/step
Saved model to disk.
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (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.
(1349, 6, 250)
(9904, 6, 250)

[1m  1/310[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5:44[0m 1s/step
[1m 56/310[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 910us/step
[1m112/310[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 905us/step
[1m173/310[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 876us/step
[1m236/310[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 855us/step
[1m296/310[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 850us/step
[1m310/310[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step  
[1m310/310[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 4ms/step

[1m 1/43[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 16ms/step
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 18ms/step
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 18ms/step

[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 17ms/step
[1m57/89[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 903us/step
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step  
Global accuracy score (validation) = 72.19 [%]
Global F1 score (validation) = 72.79 [%]
[[0.]
 [0.]
 [0.]
 ...
 [3.]
 [3.]
 [3.]]
(1349, 1)
[[8.46512616e-02 8.81023347e-01 5.25858067e-03 2.90668551e-02]
 [7.11068749e-01 2.74993241e-01 6.43044943e-03 7.50757894e-03]
 [9.06813145e-01 9.10009593e-02 9.38418671e-04 1.24750508e-03]
 ...
 [4.96942201e-04 1.42055142e-04 1.33970188e-05 9.99347627e-01]
 [3.55245866e-04 9.24987107e-05 2.42309889e-05 9.99527991e-01]
 [5.40542416e-03 1.26823531e-02 8.74015629e-01 1.07896596e-01]]
(1349, 4)
-------------------------------------------------

Global accuracy score (train) = 85.69 [%]
Global accuracy score (test) = 75.39 [%]
Global F1 score (train) = 85.88 [%]
Global F1 score (test) = 75.82 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.61      0.56      0.59       350
MODERATE-INTENSITY       0.58      0.63      0.60       350
         SEDENTARY       0.95      0.97      0.96       350
VIGOROUS-INTENSITY       0.89      0.88      0.89       299

          accuracy                           0.75      1349
         macro avg       0.76      0.76      0.76      1349
      weighted avg       0.75      0.75      0.75      1349


Accuracy capturado en la ejecución 25: 75.39 [%]
F1-score capturado en la ejecución 25: 75.82 [%]

=== EJECUCIÓN 26 ===

--- TRAIN (ejecución 26) ---

--- TEST (ejecución 26) ---
2025-11-08 16:59:30.349876: 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`.
2025-11-08 16:59:30.361334: 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 00:00:1762617570.374405 1235432 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762617570.378678 1235432 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762617570.388468 1235432 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762617570.388493 1235432 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762617570.388495 1235432 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762617570.388497 1235432 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-08 16:59:30.391698: 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 00:00:1762617572.725378 1235432 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13760 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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Epoch 1/17
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762617574.491676 1235562 service.cc:152] XLA service 0x77299c003db0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762617574.491707 1235562 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-08 16:59:34.525806: 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 00:00:1762617574.742651 1235562 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762617576.659451 1235562 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 2/17

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Epoch 3/17

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Epoch 4/17

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Epoch 5/17

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[1m 99/619[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.7993 - loss: 0.4602
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Epoch 6/17

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[1m 95/619[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.8273 - loss: 0.4929
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Epoch 7/17

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[1m 31/619[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.8534 - loss: 0.3925  
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[1m 98/619[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.8583 - loss: 0.3907
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Epoch 8/17

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[1m 1/43[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m38s[0m 928ms/step
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 20ms/step  
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 20ms/step
Saved model to disk.
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (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.
(1349, 6, 250)
(9904, 6, 250)

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[1m 1/43[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 16ms/step
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 17ms/step
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 18ms/step

[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m57/89[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 896us/step
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step  
Global accuracy score (validation) = 72.05 [%]
Global F1 score (validation) = 72.55 [%]
[[0.]
 [0.]
 [0.]
 ...
 [3.]
 [3.]
 [3.]]
(1349, 1)
[[9.4801593e-01 4.3132503e-02 7.1391594e-03 1.7123608e-03]
 [8.1964356e-01 5.2957661e-02 1.1846661e-01 8.9322152e-03]
 [9.4734412e-01 4.3819807e-02 7.1402364e-03 1.6958448e-03]
 ...
 [9.2362592e-05 1.0145478e-04 1.9996558e-05 9.9978614e-01]
 [8.1287732e-04 1.9814530e-03 5.8717438e-04 9.9661851e-01]
 [3.4550410e-03 6.4887875e-03 9.7095180e-01 1.9104363e-02]]
(1349, 4)
-------------------------------------------------

Global accuracy score (train) = 83.78 [%]
Global accuracy score (test) = 73.02 [%]
Global F1 score (train) = 84.17 [%]
Global F1 score (test) = 74.22 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.55      0.54      0.55       350
MODERATE-INTENSITY       0.58      0.71      0.64       350
         SEDENTARY       0.97      0.83      0.90       350
VIGOROUS-INTENSITY       0.92      0.86      0.89       299

          accuracy                           0.73      1349
         macro avg       0.76      0.74      0.74      1349
      weighted avg       0.75      0.73      0.74      1349


Accuracy capturado en la ejecución 26: 73.02 [%]
F1-score capturado en la ejecución 26: 74.22 [%]

=== EJECUCIÓN 27 ===

--- TRAIN (ejecución 27) ---

--- TEST (ejecución 27) ---
2025-11-08 16:59:59.560067: 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`.
2025-11-08 16:59:59.571185: 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 00:00:1762617599.584248 1237147 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762617599.588378 1237147 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762617599.598183 1237147 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762617599.598203 1237147 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762617599.598213 1237147 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762617599.598215 1237147 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-08 16:59:59.601426: 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 00:00:1762617601.919346 1237147 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13760 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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Epoch 1/17
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762617603.719390 1237273 service.cc:152] XLA service 0x7874a4012b60 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762617603.719443 1237273 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-08 17:00:03.757859: 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 00:00:1762617603.982351 1237273 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762617605.882975 1237273 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/619[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m33:38[0m 3s/step - accuracy: 0.5000 - loss: 1.4225
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[1m 61/619[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5141 - loss: 1.2923
[1m 92/619[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5198 - loss: 1.2524
[1m127/619[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5249 - loss: 1.2218
[1m161/619[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5297 - loss: 1.1990
[1m195/619[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5340 - loss: 1.1798
[1m227/619[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5374 - loss: 1.1652
[1m259/619[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5403 - loss: 1.1521
[1m294/619[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5430 - loss: 1.1399
[1m327/619[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5455 - loss: 1.1295
[1m359/619[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5481 - loss: 1.1200
[1m392/619[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5508 - loss: 1.1106
[1m425/619[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5537 - loss: 1.1014
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Epoch 2/17

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Epoch 3/17

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Epoch 4/17

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[1m 96/619[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.7743 - loss: 0.5625
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Epoch 5/17

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Epoch 6/17

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[1m298/619[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.8349 - loss: 0.4466
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Epoch 7/17

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Epoch 8/17

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[1m 31/619[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.8960 - loss: 0.3552  
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[1m 95/619[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.8823 - loss: 0.3755
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Epoch 9/17

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[1m 1/43[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m38s[0m 926ms/step
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Saved model to disk.
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Loaded model from disk.
(1349, 6, 250)
(9904, 6, 250)

[1m  1/310[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5:48[0m 1s/step
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[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m58/89[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 879us/step
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step  
Global accuracy score (validation) = 73.1 [%]
Global F1 score (validation) = 73.89 [%]
[[0.]
 [0.]
 [0.]
 ...
 [3.]
 [3.]
 [3.]]
(1349, 1)
[[9.24864590e-01 7.36951232e-02 4.63919278e-04 9.76295560e-04]
 [1.09314516e-01 6.34285212e-01 2.67046830e-03 2.53729820e-01]
 [7.49849081e-01 2.47203410e-01 1.71309686e-04 2.77622300e-03]
 ...
 [6.21992047e-04 7.45597237e-04 3.51930648e-04 9.98280525e-01]
 [7.44265853e-04 1.71280655e-04 1.77895097e-04 9.98906612e-01]
 [3.06304190e-02 3.56935672e-02 8.53961051e-01 7.97149837e-02]]
(1349, 4)
-------------------------------------------------

Global accuracy score (train) = 86.02 [%]
Global accuracy score (test) = 75.61 [%]
Global F1 score (train) = 86.23 [%]
Global F1 score (test) = 76.38 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.62      0.54      0.58       350
MODERATE-INTENSITY       0.56      0.69      0.62       350
         SEDENTARY       0.97      0.95      0.96       350
VIGOROUS-INTENSITY       0.94      0.86      0.90       299

          accuracy                           0.76      1349
         macro avg       0.77      0.76      0.76      1349
      weighted avg       0.77      0.76      0.76      1349


Accuracy capturado en la ejecución 27: 75.61 [%]
F1-score capturado en la ejecución 27: 76.38 [%]

=== EJECUCIÓN 28 ===

--- TRAIN (ejecución 28) ---

--- TEST (ejecución 28) ---
2025-11-08 17:00:30.091140: 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`.
2025-11-08 17:00:30.102639: 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 00:00:1762617630.116129 1238969 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762617630.120261 1238969 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762617630.130448 1238969 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762617630.130469 1238969 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762617630.130471 1238969 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762617630.130473 1238969 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-08 17:00:30.133786: 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 00:00:1762617632.448838 1238969 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13760 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (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 1/17
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762617634.205476 1239097 service.cc:152] XLA service 0x7a0e28005c40 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762617634.205517 1239097 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-08 17:00:34.241571: 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 00:00:1762617634.471658 1239097 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762617636.346424 1239097 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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[1m483/619[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5474 - loss: 1.0748
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[1m619/619[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m5s[0m 4ms/step - accuracy: 0.5568 - loss: 1.0481 - val_accuracy: 0.6931 - val_loss: 0.7252
Epoch 2/17

[1m  1/619[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 19ms/step - accuracy: 0.5625 - loss: 1.0714
[1m 34/619[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6125 - loss: 0.8821  
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[1m 99/619[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6407 - loss: 0.8185
[1m131/619[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6479 - loss: 0.8066
[1m165/619[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6527 - loss: 0.7970
[1m198/619[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6561 - loss: 0.7901
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Epoch 3/17

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Epoch 4/17

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Epoch 5/17

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[1m 68/619[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.7768 - loss: 0.5807
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[1m274/619[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.7781 - loss: 0.5565
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Epoch 6/17

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Epoch 7/17

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[1m 31/619[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.8791 - loss: 0.4115  
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[1m 98/619[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.8640 - loss: 0.4178
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[1m231/619[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.8546 - loss: 0.4199
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Epoch 8/17

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Epoch 9/17

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[1m 94/619[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.8820 - loss: 0.3279
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Saved model to disk.
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Loaded model from disk.
(1349, 6, 250)
(9904, 6, 250)

[1m  1/310[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5:46[0m 1s/step
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[1m 1/43[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 16ms/step
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 18ms/step
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 18ms/step

[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 23ms/step
[1m54/89[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 946us/step
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step  
Global accuracy score (validation) = 73.07 [%]
Global F1 score (validation) = 73.62 [%]
[[0.]
 [0.]
 [0.]
 ...
 [3.]
 [3.]
 [3.]]
(1349, 1)
[[9.5559603e-01 2.9380657e-02 2.7805290e-03 1.2242686e-02]
 [9.6822649e-01 2.8318739e-02 5.9257739e-04 2.8621482e-03]
 [9.6322387e-01 2.3190388e-02 3.5228541e-03 1.0062905e-02]
 ...
 [2.9083923e-05 1.9069228e-05 4.3671153e-06 9.9994743e-01]
 [1.1293783e-03 1.0804310e-03 2.9475518e-04 9.9749547e-01]
 [4.5812735e-03 3.2270625e-03 9.8754042e-01 4.6512657e-03]]
(1349, 4)
-------------------------------------------------

Global accuracy score (train) = 87.17 [%]
Global accuracy score (test) = 72.57 [%]
Global F1 score (train) = 87.38 [%]
Global F1 score (test) = 72.87 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.54      0.54      0.54       350
MODERATE-INTENSITY       0.56      0.53      0.55       350
         SEDENTARY       0.91      0.98      0.94       350
VIGOROUS-INTENSITY       0.90      0.87      0.88       299

          accuracy                           0.73      1349
         macro avg       0.73      0.73      0.73      1349
      weighted avg       0.72      0.73      0.72      1349


Accuracy capturado en la ejecución 28: 72.57 [%]
F1-score capturado en la ejecución 28: 72.87 [%]

=== EJECUCIÓN 29 ===

--- TRAIN (ejecución 29) ---

--- TEST (ejecución 29) ---
2025-11-08 17:01:00.362851: 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`.
2025-11-08 17:01:00.374101: 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 00:00:1762617660.387468 1240782 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762617660.391648 1240782 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762617660.401567 1240782 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762617660.401590 1240782 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762617660.401592 1240782 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762617660.401594 1240782 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-08 17:01:00.404814: 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 00:00:1762617662.758790 1240782 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13760 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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Epoch 1/17
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762617664.526549 1240906 service.cc:152] XLA service 0x77f718010e10 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762617664.526592 1240906 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-08 17:01:04.562042: 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 00:00:1762617664.791118 1240906 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762617666.724379 1240906 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/619[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m33:56[0m 3s/step - accuracy: 0.0000e+00 - loss: 2.1748
[1m 26/619[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.4389 - loss: 1.5245      
[1m 60/619[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4784 - loss: 1.3989
[1m 89/619[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4846 - loss: 1.3508
[1m121/619[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4918 - loss: 1.3110
[1m153/619[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5005 - loss: 1.2783
[1m187/619[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5078 - loss: 1.2505
[1m221/619[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5142 - loss: 1.2275
[1m253/619[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5195 - loss: 1.2085
[1m285/619[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5241 - loss: 1.1922
[1m318/619[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5284 - loss: 1.1772
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[1m383/619[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5355 - loss: 1.1523
[1m417/619[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5387 - loss: 1.1408
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Epoch 2/17

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Epoch 3/17

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[1m 31/619[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.7351 - loss: 0.6137  
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[1m 96/619[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.7472 - loss: 0.6036
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Epoch 4/17

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[1m 96/619[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.7844 - loss: 0.5413
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Epoch 5/17

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[1m 99/619[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.8442 - loss: 0.4476
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Epoch 6/17

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[1m 31/619[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.8226 - loss: 0.4388  
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Epoch 7/17

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[1m 1/43[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m38s[0m 926ms/step
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 20ms/step  
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 21ms/step
Saved model to disk.
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (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.
(1349, 6, 250)
(9904, 6, 250)

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[1m310/310[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step  
[1m310/310[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 3ms/step

[1m 1/43[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 15ms/step
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 18ms/step
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 19ms/step

[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 17ms/step
[1m61/89[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 836us/step
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 957us/step
Global accuracy score (validation) = 71.1 [%]
Global F1 score (validation) = 71.57 [%]
[[0.]
 [0.]
 [0.]
 ...
 [3.]
 [3.]
 [3.]]
(1349, 1)
[[9.1823226e-01 7.7310927e-02 2.0986747e-03 2.3581006e-03]
 [7.2029459e-01 2.7265739e-01 2.9420741e-03 4.1058725e-03]
 [9.0780044e-01 7.9077773e-02 2.4549761e-03 1.0666814e-02]
 ...
 [3.9638090e-03 3.9826962e-03 1.4083195e-03 9.9064517e-01]
 [1.0407121e-02 7.4747703e-03 9.8997180e-04 9.8112810e-01]
 [2.8628127e-03 3.7384653e-03 9.7830874e-01 1.5090016e-02]]
(1349, 4)
-------------------------------------------------

Global accuracy score (train) = 85.62 [%]
Global accuracy score (test) = 72.13 [%]
Global F1 score (train) = 85.89 [%]
Global F1 score (test) = 73.36 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.52      0.65      0.58       350
MODERATE-INTENSITY       0.56      0.51      0.53       350
         SEDENTARY       0.97      0.92      0.94       350
VIGOROUS-INTENSITY       0.96      0.81      0.88       299

          accuracy                           0.72      1349
         macro avg       0.75      0.72      0.73      1349
      weighted avg       0.74      0.72      0.73      1349


Accuracy capturado en la ejecución 29: 72.13 [%]
F1-score capturado en la ejecución 29: 73.36 [%]

=== EJECUCIÓN 30 ===

--- TRAIN (ejecución 30) ---

--- TEST (ejecución 30) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Loaded model from disk.
(1349, 6, 250)
(9904, 6, 250)

[1m  1/310[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5:51[0m 1s/step
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[1m283/310[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 895us/step
[1m310/310[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step  
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[1m 1/43[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 16ms/step
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 18ms/step
[1m43/43[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 18ms/step

[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 17ms/step
[1m49/89[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step 
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step
Global accuracy score (validation) = 72.58 [%]
Global F1 score (validation) = 72.83 [%]
[[0.]
 [0.]
 [0.]
 ...
 [3.]
 [3.]
 [3.]]
(1349, 1)
[[9.64989901e-01 3.27792056e-02 1.61670425e-04 2.06929445e-03]
 [8.70946527e-01 1.17508978e-01 4.05045925e-03 7.49403238e-03]
 [9.57939148e-01 3.77815142e-02 4.12611756e-04 3.86669021e-03]
 ...
 [7.15276576e-04 2.89633696e-04 5.02781884e-04 9.98492360e-01]
 [8.71252827e-03 6.23599160e-03 1.03399775e-03 9.84017432e-01]
 [2.82464735e-02 9.76864249e-02 7.65548170e-01 1.08518966e-01]]
(1349, 4)
-------------------------------------------------

Global accuracy score (train) = 82.85 [%]
Global accuracy score (test) = 73.83 [%]
Global F1 score (train) = 83.09 [%]
Global F1 score (test) = 75.05 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.53      0.74      0.62       350
MODERATE-INTENSITY       0.63      0.50      0.56       350
         SEDENTARY       0.98      0.86      0.91       350
VIGOROUS-INTENSITY       0.96      0.88      0.92       299

          accuracy                           0.74      1349
         macro avg       0.77      0.74      0.75      1349
      weighted avg       0.77      0.74      0.74      1349


Accuracy capturado en la ejecución 30: 73.83 [%]
F1-score capturado en la ejecución 30: 75.05 [%]

=== RESUMEN FINAL ===
Accuracies: [74.5, 77.02, 71.76, 73.54, 74.13, 70.2, 70.5, 74.5, 73.17, 75.69, 71.61, 74.13, 72.2, 74.05, 71.09, 75.76, 76.43, 75.98, 75.61, 74.5, 73.39, 73.76, 72.5, 72.05, 75.39, 73.02, 75.61, 72.57, 72.13, 73.83]
F1-scores: [75.0, 77.93, 72.5, 74.47, 74.83, 71.65, 72.0, 75.12, 74.01, 76.46, 72.74, 74.58, 73.48, 74.95, 71.58, 76.39, 77.09, 76.78, 75.94, 74.64, 73.41, 74.31, 72.22, 72.91, 75.82, 74.22, 76.38, 72.87, 73.36, 75.05]
Accuracy mean: 73.6873 | std: 1.7598
F1 mean: 74.4230 | std: 1.6713

Resultados guardados en /mnt/nvme1n2/git/uniovi-simur-wearablepermed-data/output/Paper_results/cases_dataset_PI/case_PI_CAPTURE24_acc_gyr_superclasses_CPA_METs/metrics_test.npz
