2025-11-08 10:00:50.212144: 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 10:00:50.224027: 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 00:00:1762592450.238426    1242 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762592450.242895    1242 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762592450.253558    1242 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762592450.253579    1242 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762592450.253582    1242 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762592450.253584    1242 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-08 10:00:50.256874: 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 10:00:54,492	INFO worker.py:1927 -- Started a local Ray instance.
2025-11-08 10:00:55,197	INFO tune.py:253 -- Initializing Ray automatically. For cluster usage or custom Ray initialization, call `ray.init(...)` before `Tuner(...)`.
2025-11-08 10:00:55,267	INFO trial.py:182 -- Creating a new dirname dir_6f442_1d4b because trial dirname 'dir_6f442' already exists.
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2025-11-08 10:00:55,291	INFO trial.py:182 -- Creating a new dirname dir_6f442_8254 because trial dirname 'dir_6f442' already exists.
2025-11-08 10:00:55,294	INFO trial.py:182 -- Creating a new dirname dir_6f442_5838 because trial dirname 'dir_6f442' already exists.
2025-11-08 10:00:55,297	INFO trial.py:182 -- Creating a new dirname dir_6f442_682f because trial dirname 'dir_6f442' already exists.
2025-11-08 10:00:55,300	INFO trial.py:182 -- Creating a new dirname dir_6f442_d91a because trial dirname 'dir_6f442' already exists.
2025-11-08 10:00:55,303	INFO trial.py:182 -- Creating a new dirname dir_6f442_fd64 because trial dirname 'dir_6f442' already exists.
2025-11-08 10:00:55,306	INFO trial.py:182 -- Creating a new dirname dir_6f442_05a2 because trial dirname 'dir_6f442' already exists.
2025-11-08 10:00:55,310	INFO trial.py:182 -- Creating a new dirname dir_6f442_063f because trial dirname 'dir_6f442' already exists.
2025-11-08 10:00:55,314	INFO trial.py:182 -- Creating a new dirname dir_6f442_b065 because trial dirname 'dir_6f442' already exists.
2025-11-08 10:00:55,317	INFO trial.py:182 -- Creating a new dirname dir_6f442_9d12 because trial dirname 'dir_6f442' already exists.
2025-11-08 10:00:55,323	INFO trial.py:182 -- Creating a new dirname dir_6f442_705e because trial dirname 'dir_6f442' already exists.
1 GPU(s) detected and VRAM set to crossover mode..
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Se lanza la búsqueda de hiperparámetros óptimos del modelo
╭─────────────────────────────────────────────────────────────────────╮
│ Configuration for experiment     CAPTURE24_hyperparameters_tuning   │
├─────────────────────────────────────────────────────────────────────┤
│ Search algorithm                 BasicVariantGenerator              │
│ Scheduler                        FIFOScheduler                      │
│ Number of trials                 20                                 │
╰─────────────────────────────────────────────────────────────────────╯

View detailed results here: /mnt/nvme1n2/git/uniovi-simur-wearablepermed-data/output/Paper_results/cases_dataset_M/case_M_CAPTURE24_acc_gyr_superclasses_CPA_METs/CAPTURE24_hyperparameters_tuning
To visualize your results with TensorBoard, run: `tensorboard --logdir /tmp/ray/session_2025-11-08_10-00-52_770711_1242/artifacts/2025-11-08_10-00-55/CAPTURE24_hyperparameters_tuning/driver_artifacts`

Trial status: 20 PENDING
Current time: 2025-11-08 10:00:55. 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_6f442    PENDING            2   rmsprop         relu                                   32                 64                  3                 0          6.72635e-06         24 │
│ trial_6f442    PENDING            2   adam            tanh                                   32                 32                  3                 0          0.000114484         16 │
│ trial_6f442    PENDING            3   adam            tanh                                   32                 64                  5                 1          7.68749e-05         27 │
│ trial_6f442    PENDING            3   rmsprop         relu                                   32                 32                  5                 1          9.37956e-06         17 │
│ trial_6f442    PENDING            2   adam            relu                                   32                 32                  3                 1          8.70224e-06         15 │
│ trial_6f442    PENDING            2   rmsprop         tanh                                   32                 16                  5                 0          2.22726e-05         22 │
│ trial_6f442    PENDING            3   rmsprop         relu                                   16                 16                  3                 0          0.000196867         23 │
│ trial_6f442    PENDING            3   adam            tanh                                   16                 64                  3                 1          1.25914e-05         23 │
│ trial_6f442    PENDING            2   adam            tanh                                   16                 16                  5                 1          5.51628e-05         28 │
│ trial_6f442    PENDING            2   rmsprop         relu                                   16                 16                  5                 1          0.000135398         18 │
│ trial_6f442    PENDING            3   adam            relu                                   16                 16                  3                 1          1.37357e-05         27 │
│ trial_6f442    PENDING            2   adam            relu                                   32                 32                  5                 1          1.00068e-05         24 │
│ trial_6f442    PENDING            2   rmsprop         tanh                                   16                 64                  5                 1          0.00018307          18 │
│ trial_6f442    PENDING            3   adam            relu                                   16                 32                  3                 1          9.69082e-05         19 │
│ trial_6f442    PENDING            3   rmsprop         tanh                                   32                 16                  5                 0          8.77002e-06         18 │
│ trial_6f442    PENDING            2   adam            tanh                                   16                 16                  5                 0          5.34611e-06         27 │
│ trial_6f442    PENDING            2   rmsprop         tanh                                   16                 16                  5                 0          2.27149e-05         28 │
│ trial_6f442    PENDING            2   rmsprop         relu                                   32                 16                  3                 0          1.65226e-05         24 │
│ trial_6f442    PENDING            2   adam            tanh                                   32                 64                  3                 0          5.15328e-05         21 │
│ trial_6f442    PENDING            2   adam            relu                                   32                 64                  3                 1          0.000128258         25 │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

Trial trial_6f442 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_6f442 config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                            16 │
│ funcion_activacion              tanh │
│ num_resblocks                      0 │
│ numero_filtros                    32 │
│ optimizador                     adam │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00011 │
╰──────────────────────────────────────╯
Trial trial_6f442 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_6f442 config             │
├──────────────────────────────────────┤
│ N_capas                            3 │
│ epochs                            23 │
│ funcion_activacion              relu │
│ num_resblocks                      0 │
│ numero_filtros                    16 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  16 │
│ tasa_aprendizaje              0.0002 │
╰──────────────────────────────────────╯
Trial trial_6f442 started with configuration:
╭─────────────────────────────────────╮
│ Trial trial_6f442 config            │
├─────────────────────────────────────┤
│ N_capas                           3 │
│ epochs                           19 │
│ funcion_activacion             relu │
│ num_resblocks                     1 │
│ numero_filtros                   32 │
│ optimizador                    adam │
│ tamanho_filtro                    3 │
│ tamanho_minilote                 16 │
│ tasa_aprendizaje             0.0001 │
╰─────────────────────────────────────╯
Trial trial_6f442 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_6f442 config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                            24 │
│ funcion_activacion              relu │
│ num_resblocks                      0 │
│ numero_filtros                    16 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00002 │
╰──────────────────────────────────────╯
Trial trial_6f442 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_6f442 config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                            24 │
│ funcion_activacion              relu │
│ num_resblocks                      0 │
│ numero_filtros                    64 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00001 │
╰──────────────────────────────────────╯
Trial trial_6f442 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_6f442 config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                            28 │
│ funcion_activacion              tanh │
│ num_resblocks                      1 │
│ numero_filtros                    16 │
│ optimizador                     adam │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  16 │
│ tasa_aprendizaje             0.00006 │
╰──────────────────────────────────────╯
Trial trial_6f442 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_6f442 config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                            24 │
│ funcion_activacion              relu │
│ num_resblocks                      1 │
│ numero_filtros                    32 │
│ optimizador                     adam │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00001 │
╰──────────────────────────────────────╯
Trial trial_6f442 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_6f442 config             │
├──────────────────────────────────────┤
│ N_capas                            3 │
│ epochs                            23 │
│ funcion_activacion              tanh │
│ num_resblocks                      1 │
│ numero_filtros                    64 │
│ optimizador                     adam │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  16 │
│ tasa_aprendizaje             0.00001 │
╰──────────────────────────────────────╯
Trial trial_6f442 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_6f442 config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                            18 │
│ funcion_activacion              relu │
│ num_resblocks                      1 │
│ numero_filtros                    16 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  16 │
│ tasa_aprendizaje             0.00014 │
╰──────────────────────────────────────╯
Trial trial_6f442 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_6f442 config             │
├──────────────────────────────────────┤
│ N_capas                            3 │
│ epochs                            27 │
│ funcion_activacion              tanh │
│ num_resblocks                      1 │
│ numero_filtros                    64 │
│ optimizador                     adam │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00008 │
╰──────────────────────────────────────╯
Trial trial_6f442 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_6f442 config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                            27 │
│ funcion_activacion              tanh │
│ num_resblocks                      0 │
│ numero_filtros                    16 │
│ optimizador                     adam │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  16 │
│ tasa_aprendizaje             0.00001 │
╰──────────────────────────────────────╯
Trial trial_6f442 started with configuration:
[36m(train_cnn_ray_tune pid=3051)[0m 2025-11-08 10:00:58.496473: 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=3051)[0m 2025-11-08 10:00:58.518264: 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=3051)[0m WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
[36m(train_cnn_ray_tune pid=3051)[0m E0000 00:00:1762592458.547214    4267 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=3051)[0m E0000 00:00:1762592458.555432    4267 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=3051)[0m W0000 00:00:1762592458.575992    4267 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=3051)[0m W0000 00:00:1762592458.576046    4267 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=3051)[0m W0000 00:00:1762592458.576048    4267 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=3051)[0m W0000 00:00:1762592458.576050    4267 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=3051)[0m 2025-11-08 10:00:58.582019: 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=3051)[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=3051)[0m 2025-11-08 10:01:01.665807: 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=3051)[0m 2025-11-08 10:01:01.665851: I external/local_xla/xla/stream_executor/cuda/cuda_diagnostics.cc:167] env: CUDA_VISIBLE_DEVICES=""
[36m(train_cnn_ray_tune pid=3051)[0m 2025-11-08 10:01:01.665859: 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=3051)[0m 2025-11-08 10:01:01.665864: 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=3051)[0m 2025-11-08 10:01:01.665869: 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=3051)[0m 2025-11-08 10:01:01.665872: I external/local_xla/xla/stream_executor/cuda/cuda_diagnostics.cc:190] hostname: simur-MS-7B94
[36m(train_cnn_ray_tune pid=3051)[0m 2025-11-08 10:01:01.666080: 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=3051)[0m 2025-11-08 10:01:01.666106: 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=3051)[0m 2025-11-08 10:01:01.666110: I external/local_xla/xla/stream_executor/cuda/cuda_diagnostics.cc:291] kernel version seems to match DSO: 570.133.7
╭──────────────────────────────────────╮
│ Trial trial_6f442 config             │
├──────────────────────────────────────┤
│ N_capas                            3 │
│ epochs                            18 │
│ funcion_activacion              tanh │
│ num_resblocks                      0 │
│ numero_filtros                    16 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00001 │
╰──────────────────────────────────────╯
Trial trial_6f442 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_6f442 config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                            21 │
│ funcion_activacion              tanh │
│ num_resblocks                      0 │
│ numero_filtros                    64 │
│ optimizador                     adam │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00005 │
╰──────────────────────────────────────╯
Trial trial_6f442 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_6f442 config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                            15 │
│ funcion_activacion              relu │
│ num_resblocks                      1 │
│ numero_filtros                    32 │
│ optimizador                     adam │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00001 │
╰──────────────────────────────────────╯
Trial trial_6f442 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_6f442 config             │
├──────────────────────────────────────┤
│ N_capas                            3 │
│ epochs                            17 │
│ funcion_activacion              relu │
│ num_resblocks                      1 │
│ numero_filtros                    32 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00001 │
╰──────────────────────────────────────╯
Trial trial_6f442 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_6f442 config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                            28 │
│ funcion_activacion              tanh │
│ num_resblocks                      0 │
│ numero_filtros                    16 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  16 │
│ tasa_aprendizaje             0.00002 │
╰──────────────────────────────────────╯
Trial trial_6f442 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_6f442 config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                            22 │
│ funcion_activacion              tanh │
│ num_resblocks                      0 │
│ numero_filtros                    16 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00002 │
╰──────────────────────────────────────╯
Trial trial_6f442 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_6f442 config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                            18 │
│ funcion_activacion              tanh │
│ num_resblocks                      1 │
│ numero_filtros                    64 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  16 │
│ tasa_aprendizaje             0.00018 │
╰──────────────────────────────────────╯
Trial trial_6f442 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_6f442 config             │
├──────────────────────────────────────┤
│ N_capas                            3 │
│ epochs                            27 │
│ funcion_activacion              relu │
│ num_resblocks                      1 │
│ numero_filtros                    16 │
│ optimizador                     adam │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  16 │
│ tasa_aprendizaje             0.00001 │
╰──────────────────────────────────────╯
Trial trial_6f442 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_6f442 config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                            25 │
│ funcion_activacion              relu │
│ num_resblocks                      1 │
│ numero_filtros                    64 │
│ optimizador                     adam │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00013 │
╰──────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3051)[0m Epoch 1/16
[36m(train_cnn_ray_tune pid=3022)[0m 
[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m17:54[0m 3s/step - accuracy: 0.2500 - loss: 1.7044
[36m(train_cnn_ray_tune pid=3022)[0m 
[1m  3/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 31ms/step - accuracy: 0.2691 - loss: 1.7172
[1m  5/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 30ms/step - accuracy: 0.2649 - loss: 1.7412 
[36m(train_cnn_ray_tune pid=3022)[0m 
[1m  8/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 27ms/step - accuracy: 0.2619 - loss: 1.7340
[36m(train_cnn_ray_tune pid=3023)[0m 
[1m  4/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2786 - loss: 1.8300 
[36m(train_cnn_ray_tune pid=3022)[0m 
[1m 11/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 26ms/step - accuracy: 0.2586 - loss: 1.7307
[1m 14/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 25ms/step - accuracy: 0.2600 - loss: 1.7269
[36m(train_cnn_ray_tune pid=3054)[0m 
[1m  1/655[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m45:29[0m 4s/step - accuracy: 0.0625 - loss: 2.1960
[1m  3/655[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m20s[0m 31ms/step - accuracy: 0.1285 - loss: 2.2454
[36m(train_cnn_ray_tune pid=3022)[0m 
[1m 21/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 27ms/step - accuracy: 0.2622 - loss: 1.7301
[36m(train_cnn_ray_tune pid=3066)[0m Epoch 1/28[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=3059)[0m 
[1m 24/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 32ms/step - accuracy: 0.2633 - loss: 2.0622 
[1m 26/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 32ms/step - accuracy: 0.2630 - loss: 2.0658
[36m(train_cnn_ray_tune pid=3057)[0m 
[1m  2/655[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:08[0m 106ms/step - accuracy: 0.2812 - loss: 1.8748
[36m(train_cnn_ray_tune pid=3043)[0m 
[1m 16/655[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m26s[0m 42ms/step - accuracy: 0.2421 - loss: 1.8863
[1m 17/655[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m27s[0m 42ms/step - accuracy: 0.2434 - loss: 1.8823
[1m 18/655[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m27s[0m 43ms/step - accuracy: 0.2447 - loss: 1.8786
[36m(train_cnn_ray_tune pid=3057)[0m 
[1m  3/655[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m58s[0m 90ms/step - accuracy: 0.2778 - loss: 1.8689  
[1m  4/655[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m50s[0m 78ms/step - accuracy: 0.2708 - loss: 1.8605
[36m(train_cnn_ray_tune pid=3029)[0m 
[1m  1/655[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:10:22[0m 6s/step - accuracy: 0.1250 - loss: 1.9516[32m [repeated 13x across cluster][0m
[36m(train_cnn_ray_tune pid=3064)[0m 
[1m108/655[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m14s[0m 27ms/step - accuracy: 0.2448 - loss: 1.8884
[1m110/655[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m14s[0m 27ms/step - accuracy: 0.2449 - loss: 1.8883[32m [repeated 206x across cluster][0m
[36m(train_cnn_ray_tune pid=3059)[0m 
[1m102/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m7s[0m 34ms/step - accuracy: 0.2504 - loss: 2.1278[32m [repeated 138x across cluster][0m
[36m(train_cnn_ray_tune pid=3043)[0m 
[1m 47/655[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m28s[0m 46ms/step - accuracy: 0.2699 - loss: 1.8403[32m [repeated 194x across cluster][0m
[36m(train_cnn_ray_tune pid=3059)[0m 
[1m104/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m7s[0m 34ms/step - accuracy: 0.2503 - loss: 2.1277
[1m106/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m7s[0m 34ms/step - accuracy: 0.2502 - loss: 2.1277[32m [repeated 156x across cluster][0m
[36m(train_cnn_ray_tune pid=3043)[0m 
[1m  1/655[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:10:34[0m 6s/step - accuracy: 0.2500 - loss: 1.7820
[1m  2/655[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m38s[0m 59ms/step - accuracy: 0.2500 - loss: 1.7948  [32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3060)[0m 
[1m295/655[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m9s[0m 26ms/step - accuracy: 0.3058 - loss: 1.8244
[1m297/655[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m9s[0m 26ms/step - accuracy: 0.3059 - loss: 1.8237
[1m300/655[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m9s[0m 26ms/step - accuracy: 0.3061 - loss: 1.8227
[36m(train_cnn_ray_tune pid=3029)[0m 
[1m  3/655[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m26s[0m 40ms/step - accuracy: 0.2222 - loss: 1.7608  
[36m(train_cnn_ray_tune pid=3043)[0m 
[1m118/655[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m25s[0m 47ms/step - accuracy: 0.2885 - loss: 1.7984
[1m119/655[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m25s[0m 47ms/step - accuracy: 0.2887 - loss: 1.7979
[1m120/655[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m25s[0m 47ms/step - accuracy: 0.2889 - loss: 1.7974[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3064)[0m 
[1m284/655[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m10s[0m 28ms/step - accuracy: 0.2480 - loss: 1.8772
[1m286/655[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m10s[0m 28ms/step - accuracy: 0.2480 - loss: 1.8770[32m [repeated 193x across cluster][0m
[36m(train_cnn_ray_tune pid=3053)[0m 
[1m225/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m3s[0m 37ms/step - accuracy: 0.2920 - loss: 1.8047[32m [repeated 271x across cluster][0m
[36m(train_cnn_ray_tune pid=3044)[0m 
[1m105/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m14s[0m 65ms/step - accuracy: 0.2778 - loss: 1.9459[32m [repeated 240x across cluster][0m
[36m(train_cnn_ray_tune pid=3059)[0m 
[1m246/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m2s[0m 35ms/step - accuracy: 0.2500 - loss: 2.1107
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[36m(train_cnn_ray_tune pid=3054)[0m 
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[36m(train_cnn_ray_tune pid=3054)[0m .3081 - loss: 1.8429
[36m(train_cnn_ray_tune pid=3023)[0m 
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[36m(train_cnn_ray_tune pid=3023)[0m Epoch 2/24
[36m(train_cnn_ray_tune pid=3023)[0m 
[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m26s[0m 82ms/step - accuracy: 0.3438 - loss: 1.6691
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[36m(train_cnn_ray_tune pid=3066)[0m 
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[36m(train_cnn_ray_tune pid=3058)[0m 
[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m50s[0m 154ms/step - accuracy: 0.2812 - loss: 1.7952
[36m(train_cnn_ray_tune pid=3058)[0m 
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[36m(train_cnn_ray_tune pid=3022)[0m 
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[36m(train_cnn_ray_tune pid=3051)[0m 
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[36m(train_cnn_ray_tune pid=3061)[0m 
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[36m(train_cnn_ray_tune pid=3022)[0m 
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[36m(train_cnn_ray_tune pid=3061)[0m 
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[36m(train_cnn_ray_tune pid=3043)[0m 
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[36m(train_cnn_ray_tune pid=3023)[0m 
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[36m(train_cnn_ray_tune pid=3043)[0m 
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[36m(train_cnn_ray_tune pid=3062)[0m 
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m19s[0m 45ms/step - accuracy: 0.3474 - loss: 1.5900 - val_accuracy: 0.4424 - val_loss: 1.3459[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=3062)[0m Epoch 2/25[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=3062)[0m 
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[36m(train_cnn_ray_tune pid=3053)[0m 
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Trial status: 20 RUNNING
Current time: 2025-11-08 10:01:25. 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_6f442    RUNNING            2   rmsprop         relu                                   32                 64                  3                 0          6.72635e-06         24 │
│ trial_6f442    RUNNING            2   adam            tanh                                   32                 32                  3                 0          0.000114484         16 │
│ trial_6f442    RUNNING            3   adam            tanh                                   32                 64                  5                 1          7.68749e-05         27 │
│ trial_6f442    RUNNING            3   rmsprop         relu                                   32                 32                  5                 1          9.37956e-06         17 │
│ trial_6f442    RUNNING            2   adam            relu                                   32                 32                  3                 1          8.70224e-06         15 │
│ trial_6f442    RUNNING            2   rmsprop         tanh                                   32                 16                  5                 0          2.22726e-05         22 │
│ trial_6f442    RUNNING            3   rmsprop         relu                                   16                 16                  3                 0          0.000196867         23 │
│ trial_6f442    RUNNING            3   adam            tanh                                   16                 64                  3                 1          1.25914e-05         23 │
│ trial_6f442    RUNNING            2   adam            tanh                                   16                 16                  5                 1          5.51628e-05         28 │
│ trial_6f442    RUNNING            2   rmsprop         relu                                   16                 16                  5                 1          0.000135398         18 │
│ trial_6f442    RUNNING            3   adam            relu                                   16                 16                  3                 1          1.37357e-05         27 │
│ trial_6f442    RUNNING            2   adam            relu                                   32                 32                  5                 1          1.00068e-05         24 │
│ trial_6f442    RUNNING            2   rmsprop         tanh                                   16                 64                  5                 1          0.00018307          18 │
│ trial_6f442    RUNNING            3   adam            relu                                   16                 32                  3                 1          9.69082e-05         19 │
│ trial_6f442    RUNNING            3   rmsprop         tanh                                   32                 16                  5                 0          8.77002e-06         18 │
│ trial_6f442    RUNNING            2   adam            tanh                                   16                 16                  5                 0          5.34611e-06         27 │
│ trial_6f442    RUNNING            2   rmsprop         tanh                                   16                 16                  5                 0          2.27149e-05         28 │
│ trial_6f442    RUNNING            2   rmsprop         relu                                   32                 16                  3                 0          1.65226e-05         24 │
│ trial_6f442    RUNNING            2   adam            tanh                                   32                 64                  3                 0          5.15328e-05         21 │
│ trial_6f442    RUNNING            2   adam            relu                                   32                 64                  3                 1          0.000128258         25 │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3062)[0m 
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[36m(train_cnn_ray_tune pid=3066)[0m 
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[36m(train_cnn_ray_tune pid=3061)[0m 
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[36m(train_cnn_ray_tune pid=3063)[0m 
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[36m(train_cnn_ray_tune pid=3061)[0m 
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[36m(train_cnn_ray_tune pid=3059)[0m 
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[36m(train_cnn_ray_tune pid=3060)[0m 
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[36m(train_cnn_ray_tune pid=3060)[0m Epoch 2/18[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3060)[0m 
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[36m(train_cnn_ray_tune pid=3023)[0m 
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[36m(train_cnn_ray_tune pid=3055)[0m 
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[36m(train_cnn_ray_tune pid=3023)[0m 
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[36m(train_cnn_ray_tune pid=3058)[0m 
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[36m(train_cnn_ray_tune pid=3060)[0m 
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[36m(train_cnn_ray_tune pid=3061)[0m 
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[36m(train_cnn_ray_tune pid=3064)[0m 
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[36m(train_cnn_ray_tune pid=3058)[0m 
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[36m(train_cnn_ray_tune pid=3064)[0m 
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[36m(train_cnn_ray_tune pid=3044)[0m 
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[36m(train_cnn_ray_tune pid=3044)[0m Epoch 2/27[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=3023)[0m 
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[36m(train_cnn_ray_tune pid=3044)[0m 
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[36m(train_cnn_ray_tune pid=3054)[0m 
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[36m(train_cnn_ray_tune pid=3054)[0m 
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[36m(train_cnn_ray_tune pid=3062)[0m 
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[36m(train_cnn_ray_tune pid=3054)[0m 
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[36m(train_cnn_ray_tune pid=3057)[0m 
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[36m(train_cnn_ray_tune pid=3053)[0m 
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[36m(train_cnn_ray_tune pid=3064)[0m 
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[36m(train_cnn_ray_tune pid=3063)[0m 
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[36m(train_cnn_ray_tune pid=3053)[0m 
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[36m(train_cnn_ray_tune pid=3053)[0m Epoch 3/21[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=3065)[0m 
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[36m(train_cnn_ray_tune pid=3029)[0m 
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[36m(train_cnn_ray_tune pid=3044)[0m 
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[36m(train_cnn_ray_tune pid=3063)[0m 
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[36m(train_cnn_ray_tune pid=3053)[0m 
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[36m(train_cnn_ray_tune pid=3061)[0m 
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[36m(train_cnn_ray_tune pid=3044)[0m 
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[36m(train_cnn_ray_tune pid=3058)[0m 
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[36m(train_cnn_ray_tune pid=3043)[0m 
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[36m(train_cnn_ray_tune pid=3043)[0m Epoch 2/19[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3052)[0m 
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[36m(train_cnn_ray_tune pid=3063)[0m 
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[36m(train_cnn_ray_tune pid=3043)[0m 
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[36m(train_cnn_ray_tune pid=3022)[0m 
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[36m(train_cnn_ray_tune pid=3065)[0m 
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[36m(train_cnn_ray_tune pid=3059)[0m 
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[36m(train_cnn_ray_tune pid=3043)[0m 
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[36m(train_cnn_ray_tune pid=3023)[0m 
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[36m(train_cnn_ray_tune pid=3066)[0m 
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[36m(train_cnn_ray_tune pid=3055)[0m 
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[36m(train_cnn_ray_tune pid=3062)[0m 
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[36m(train_cnn_ray_tune pid=3062)[0m Epoch 4/25[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=3065)[0m 
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[36m(train_cnn_ray_tune pid=3062)[0m 
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[36m(train_cnn_ray_tune pid=3057)[0m 
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[36m(train_cnn_ray_tune pid=3064)[0m 
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[36m(train_cnn_ray_tune pid=3065)[0m 
[36m(train_cnn_ray_tune pid=3065)[0m 
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[36m(train_cnn_ray_tune pid=3055)[0m 
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[36m(train_cnn_ray_tune pid=3059)[0m 
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[36m(train_cnn_ray_tune pid=3066)[0m 
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[36m(train_cnn_ray_tune pid=3062)[0m 
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[36m(train_cnn_ray_tune pid=3058)[0m 
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[36m(train_cnn_ray_tune pid=3058)[0m Epoch 5/22[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3022)[0m 
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[36m(train_cnn_ray_tune pid=3066)[0m 
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[36m(train_cnn_ray_tune pid=3061)[0m 
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Trial status: 20 RUNNING
Current time: 2025-11-08 10:01:55. Total running time: 1min 0s
Logical resource usage: 20.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status       N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     num_resblocks     tasa_aprendizaje     epochs │
├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_6f442    RUNNING            2   rmsprop         relu                                   32                 64                  3                 0          6.72635e-06         24 │
│ trial_6f442    RUNNING            2   adam            tanh                                   32                 32                  3                 0          0.000114484         16 │
│ trial_6f442    RUNNING            3   adam            tanh                                   32                 64                  5                 1          7.68749e-05         27 │
│ trial_6f442    RUNNING            3   rmsprop         relu                                   32                 32                  5                 1          9.37956e-06         17 │
│ trial_6f442    RUNNING            2   adam            relu                                   32                 32                  3                 1          8.70224e-06         15 │
│ trial_6f442    RUNNING            2   rmsprop         tanh                                   32                 16                  5                 0          2.22726e-05         22 │
│ trial_6f442    RUNNING            3   rmsprop         relu                                   16                 16                  3                 0          0.000196867         23 │
│ trial_6f442    RUNNING            3   adam            tanh                                   16                 64                  3                 1          1.25914e-05         23 │
│ trial_6f442    RUNNING            2   adam            tanh                                   16                 16                  5                 1          5.51628e-05         28 │
│ trial_6f442    RUNNING            2   rmsprop         relu                                   16                 16                  5                 1          0.000135398         18 │
│ trial_6f442    RUNNING            3   adam            relu                                   16                 16                  3                 1          1.37357e-05         27 │
│ trial_6f442    RUNNING            2   adam            relu                                   32                 32                  5                 1          1.00068e-05         24 │
│ trial_6f442    RUNNING            2   rmsprop         tanh                                   16                 64                  5                 1          0.00018307          18 │
│ trial_6f442    RUNNING            3   adam            relu                                   16                 32                  3                 1          9.69082e-05         19 │
│ trial_6f442    RUNNING            3   rmsprop         tanh                                   32                 16                  5                 0          8.77002e-06         18 │
│ trial_6f442    RUNNING            2   adam            tanh                                   16                 16                  5                 0          5.34611e-06         27 │
│ trial_6f442    RUNNING            2   rmsprop         tanh                                   16                 16                  5                 0          2.27149e-05         28 │
│ trial_6f442    RUNNING            2   rmsprop         relu                                   32                 16                  3                 0          1.65226e-05         24 │
│ trial_6f442    RUNNING            2   adam            tanh                                   32                 64                  3                 0          5.15328e-05         21 │
│ trial_6f442    RUNNING            2   adam            relu                                   32                 64                  3                 1          0.000128258         25 │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3051)[0m 
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[36m(train_cnn_ray_tune pid=3043)[0m 
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[36m(train_cnn_ray_tune pid=3053)[0m 
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[36m(train_cnn_ray_tune pid=3057)[0m 
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[36m(train_cnn_ray_tune pid=3053)[0m 
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[36m(train_cnn_ray_tune pid=3065)[0m 
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[36m(train_cnn_ray_tune pid=3065)[0m Epoch 5/24[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=3043)[0m 
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[36m(train_cnn_ray_tune pid=3022)[0m 
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[36m(train_cnn_ray_tune pid=3064)[0m 
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[36m(train_cnn_ray_tune pid=3053)[0m 
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[36m(train_cnn_ray_tune pid=3066)[0m 
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[36m(train_cnn_ray_tune pid=3054)[0m 
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[36m(train_cnn_ray_tune pid=3059)[0m 
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[36m(train_cnn_ray_tune pid=3058)[0m 
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[36m(train_cnn_ray_tune pid=3058)[0m Epoch 6/22[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=3062)[0m 
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[36m(train_cnn_ray_tune pid=3062)[0m 
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[36m(train_cnn_ray_tune pid=3022)[0m 
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[36m(train_cnn_ray_tune pid=3029)[0m 
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[36m(train_cnn_ray_tune pid=3043)[0m 
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[36m(train_cnn_ray_tune pid=3060)[0m 
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[36m(train_cnn_ray_tune pid=3062)[0m 
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[36m(train_cnn_ray_tune pid=3059)[0m 
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[36m(train_cnn_ray_tune pid=3059)[0m Epoch 6/15[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=3065)[0m 
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[36m(train_cnn_ray_tune pid=3064)[0m 
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[36m(train_cnn_ray_tune pid=3057)[0m 
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[36m(train_cnn_ray_tune pid=3064)[0m 
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[36m(train_cnn_ray_tune pid=3059)[0m 
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[36m(train_cnn_ray_tune pid=3043)[0m 
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[36m(train_cnn_ray_tune pid=3023)[0m 
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[36m(train_cnn_ray_tune pid=3023)[0m 
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[36m(train_cnn_ray_tune pid=3053)[0m 
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[36m(train_cnn_ray_tune pid=3053)[0m Epoch 6/21[32m [repeated 6x across cluster][0m
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[36m(train_cnn_ray_tune pid=3044)[0m 
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[36m(train_cnn_ray_tune pid=3052)[0m 
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[36m(train_cnn_ray_tune pid=3062)[0m 
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[36m(train_cnn_ray_tune pid=3044)[0m 
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[36m(train_cnn_ray_tune pid=3054)[0m 
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[36m(train_cnn_ray_tune pid=3043)[0m 
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[36m(train_cnn_ray_tune pid=3059)[0m 
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[36m(train_cnn_ray_tune pid=3066)[0m 
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[36m(train_cnn_ray_tune pid=3022)[0m 
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[36m(train_cnn_ray_tune pid=3022)[0m Epoch 6/24[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3066)[0m 
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[36m(train_cnn_ray_tune pid=3023)[0m 
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[36m(train_cnn_ray_tune pid=3060)[0m 
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[36m(train_cnn_ray_tune pid=3043)[0m 
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[36m(train_cnn_ray_tune pid=3023)[0m 
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[36m(train_cnn_ray_tune pid=3052)[0m 
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[36m(train_cnn_ray_tune pid=3059)[0m 
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[36m(train_cnn_ray_tune pid=3061)[0m 
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[36m(train_cnn_ray_tune pid=3066)[0m 
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[36m(train_cnn_ray_tune pid=3066)[0m 
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[36m(train_cnn_ray_tune pid=3063)[0m 
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[36m(train_cnn_ray_tune pid=3063)[0m Epoch 4/18[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=3064)[0m 
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[36m(train_cnn_ray_tune pid=3063)[0m 
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[36m(train_cnn_ray_tune pid=3058)[0m 
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[36m(train_cnn_ray_tune pid=3057)[0m 
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Trial status: 20 RUNNING
Current time: 2025-11-08 10:02:25. Total running time: 1min 30s
Logical resource usage: 20.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status       N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     num_resblocks     tasa_aprendizaje     epochs │
├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_6f442    RUNNING            2   rmsprop         relu                                   32                 64                  3                 0          6.72635e-06         24 │
│ trial_6f442    RUNNING            2   adam            tanh                                   32                 32                  3                 0          0.000114484         16 │
│ trial_6f442    RUNNING            3   adam            tanh                                   32                 64                  5                 1          7.68749e-05         27 │
│ trial_6f442    RUNNING            3   rmsprop         relu                                   32                 32                  5                 1          9.37956e-06         17 │
│ trial_6f442    RUNNING            2   adam            relu                                   32                 32                  3                 1          8.70224e-06         15 │
│ trial_6f442    RUNNING            2   rmsprop         tanh                                   32                 16                  5                 0          2.22726e-05         22 │
│ trial_6f442    RUNNING            3   rmsprop         relu                                   16                 16                  3                 0          0.000196867         23 │
│ trial_6f442    RUNNING            3   adam            tanh                                   16                 64                  3                 1          1.25914e-05         23 │
│ trial_6f442    RUNNING            2   adam            tanh                                   16                 16                  5                 1          5.51628e-05         28 │
│ trial_6f442    RUNNING            2   rmsprop         relu                                   16                 16                  5                 1          0.000135398         18 │
│ trial_6f442    RUNNING            3   adam            relu                                   16                 16                  3                 1          1.37357e-05         27 │
│ trial_6f442    RUNNING            2   adam            relu                                   32                 32                  5                 1          1.00068e-05         24 │
│ trial_6f442    RUNNING            2   rmsprop         tanh                                   16                 64                  5                 1          0.00018307          18 │
│ trial_6f442    RUNNING            3   adam            relu                                   16                 32                  3                 1          9.69082e-05         19 │
│ trial_6f442    RUNNING            3   rmsprop         tanh                                   32                 16                  5                 0          8.77002e-06         18 │
│ trial_6f442    RUNNING            2   adam            tanh                                   16                 16                  5                 0          5.34611e-06         27 │
│ trial_6f442    RUNNING            2   rmsprop         tanh                                   16                 16                  5                 0          2.27149e-05         28 │
│ trial_6f442    RUNNING            2   rmsprop         relu                                   32                 16                  3                 0          1.65226e-05         24 │
│ trial_6f442    RUNNING            2   adam            tanh                                   32                 64                  3                 0          5.15328e-05         21 │
│ trial_6f442    RUNNING            2   adam            relu                                   32                 64                  3                 1          0.000128258         25 │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3062)[0m 
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[36m(train_cnn_ray_tune pid=3059)[0m 
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[36m(train_cnn_ray_tune pid=3066)[0m 
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[36m(train_cnn_ray_tune pid=3023)[0m 
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[36m(train_cnn_ray_tune pid=3053)[0m 
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[36m(train_cnn_ray_tune pid=3053)[0m Epoch 7/21[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3066)[0m 
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[36m(train_cnn_ray_tune pid=3053)[0m 
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[36m(train_cnn_ray_tune pid=3062)[0m 
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[36m(train_cnn_ray_tune pid=3023)[0m 
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[36m(train_cnn_ray_tune pid=3023)[0m 
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[36m(train_cnn_ray_tune pid=3064)[0m 
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[36m(train_cnn_ray_tune pid=3043)[0m 
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[36m(train_cnn_ray_tune pid=3054)[0m 
[1m174/655[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m19s[0m 40ms/step - accuracy: 0.4145 - loss: 1.3033[32m [repeated 185x across cluster][0m
[36m(train_cnn_ray_tune pid=3058)[0m 
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[1m304/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 33ms/step - accuracy: 0.3314 - loss: 1.5026[32m [repeated 230x across cluster][0m
[36m(train_cnn_ray_tune pid=3029)[0m 
[1m  3/655[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m25s[0m 39ms/step - accuracy: 0.2014 - loss: 1.7412  
[36m(train_cnn_ray_tune pid=3065)[0m 
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m13s[0m 38ms/step - accuracy: 0.3646 - loss: 1.6023 - val_accuracy: 0.3982 - val_loss: 1.3693[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=3065)[0m Epoch 8/24[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=3064)[0m 
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[36m(train_cnn_ray_tune pid=3052)[0m 
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[36m(train_cnn_ray_tune pid=3051)[0m 
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[36m(train_cnn_ray_tune pid=3061)[0m 
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[36m(train_cnn_ray_tune pid=3061)[0m Epoch 7/18[32m [repeated 4x across cluster][0m
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[36m(train_cnn_ray_tune pid=3062)[0m 
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[36m(train_cnn_ray_tune pid=3059)[0m 
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[36m(train_cnn_ray_tune pid=3061)[0m 
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[36m(train_cnn_ray_tune pid=3062)[0m 
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[36m(train_cnn_ray_tune pid=3066)[0m 
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[36m(train_cnn_ray_tune pid=3066)[0m 
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[36m(train_cnn_ray_tune pid=3066)[0m Epoch 5/28[32m [repeated 5x across cluster][0m
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[36m(train_cnn_ray_tune pid=3022)[0m 
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[36m(train_cnn_ray_tune pid=3061)[0m 
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[36m(train_cnn_ray_tune pid=3060)[0m 
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[36m(train_cnn_ray_tune pid=3065)[0m 
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[36m(train_cnn_ray_tune pid=3055)[0m 
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[36m(train_cnn_ray_tune pid=3066)[0m 
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[36m(train_cnn_ray_tune pid=3063)[0m 
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[36m(train_cnn_ray_tune pid=3059)[0m 
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[36m(train_cnn_ray_tune pid=3062)[0m 
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[36m(train_cnn_ray_tune pid=3043)[0m 
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[36m(train_cnn_ray_tune pid=3022)[0m 
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[36m(train_cnn_ray_tune pid=3059)[0m 
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[36m(train_cnn_ray_tune pid=3059)[0m Epoch 9/15[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=3064)[0m 
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[36m(train_cnn_ray_tune pid=3064)[0m 
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[36m(train_cnn_ray_tune pid=3063)[0m 
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[36m(train_cnn_ray_tune pid=3059)[0m 
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[36m(train_cnn_ray_tune pid=3060)[0m 
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[36m(train_cnn_ray_tune pid=3055)[0m 
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[36m(train_cnn_ray_tune pid=3058)[0m 
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[36m(train_cnn_ray_tune pid=3064)[0m 
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[36m(train_cnn_ray_tune pid=3058)[0m 
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[36m(train_cnn_ray_tune pid=3052)[0m 
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[36m(train_cnn_ray_tune pid=3054)[0m 
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[36m(train_cnn_ray_tune pid=3057)[0m 
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[36m(train_cnn_ray_tune pid=3062)[0m 
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[36m(train_cnn_ray_tune pid=3062)[0m Epoch 9/25[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=3064)[0m 
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Trial status: 20 RUNNING
Current time: 2025-11-08 10:02:55. Total running time: 2min 0s
Logical resource usage: 20.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status       N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     num_resblocks     tasa_aprendizaje     epochs │
├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_6f442    RUNNING            2   rmsprop         relu                                   32                 64                  3                 0          6.72635e-06         24 │
│ trial_6f442    RUNNING            2   adam            tanh                                   32                 32                  3                 0          0.000114484         16 │
│ trial_6f442    RUNNING            3   adam            tanh                                   32                 64                  5                 1          7.68749e-05         27 │
│ trial_6f442    RUNNING            3   rmsprop         relu                                   32                 32                  5                 1          9.37956e-06         17 │
│ trial_6f442    RUNNING            2   adam            relu                                   32                 32                  3                 1          8.70224e-06         15 │
│ trial_6f442    RUNNING            2   rmsprop         tanh                                   32                 16                  5                 0          2.22726e-05         22 │
│ trial_6f442    RUNNING            3   rmsprop         relu                                   16                 16                  3                 0          0.000196867         23 │
│ trial_6f442    RUNNING            3   adam            tanh                                   16                 64                  3                 1          1.25914e-05         23 │
│ trial_6f442    RUNNING            2   adam            tanh                                   16                 16                  5                 1          5.51628e-05         28 │
│ trial_6f442    RUNNING            2   rmsprop         relu                                   16                 16                  5                 1          0.000135398         18 │
│ trial_6f442    RUNNING            3   adam            relu                                   16                 16                  3                 1          1.37357e-05         27 │
│ trial_6f442    RUNNING            2   adam            relu                                   32                 32                  5                 1          1.00068e-05         24 │
│ trial_6f442    RUNNING            2   rmsprop         tanh                                   16                 64                  5                 1          0.00018307          18 │
│ trial_6f442    RUNNING            3   adam            relu                                   16                 32                  3                 1          9.69082e-05         19 │
│ trial_6f442    RUNNING            3   rmsprop         tanh                                   32                 16                  5                 0          8.77002e-06         18 │
│ trial_6f442    RUNNING            2   adam            tanh                                   16                 16                  5                 0          5.34611e-06         27 │
│ trial_6f442    RUNNING            2   rmsprop         tanh                                   16                 16                  5                 0          2.27149e-05         28 │
│ trial_6f442    RUNNING            2   rmsprop         relu                                   32                 16                  3                 0          1.65226e-05         24 │
│ trial_6f442    RUNNING            2   adam            tanh                                   32                 64                  3                 0          5.15328e-05         21 │
│ trial_6f442    RUNNING            2   adam            relu                                   32                 64                  3                 1          0.000128258         25 │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3062)[0m 
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[36m(train_cnn_ray_tune pid=3053)[0m 
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[36m(train_cnn_ray_tune pid=3023)[0m 
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[36m(train_cnn_ray_tune pid=3052)[0m 
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[36m(train_cnn_ray_tune pid=3023)[0m 
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[36m(train_cnn_ray_tune pid=3053)[0m 
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[36m(train_cnn_ray_tune pid=3053)[0m 
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[36m(train_cnn_ray_tune pid=3062)[0m 
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[36m(train_cnn_ray_tune pid=3052)[0m 
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[36m(train_cnn_ray_tune pid=3044)[0m 
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[36m(train_cnn_ray_tune pid=3044)[0m Epoch 5/27[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3065)[0m 
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[36m(train_cnn_ray_tune pid=3059)[0m 
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[36m(train_cnn_ray_tune pid=3064)[0m 
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[36m(train_cnn_ray_tune pid=3043)[0m 
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[36m(train_cnn_ray_tune pid=3059)[0m 
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[36m(train_cnn_ray_tune pid=3054)[0m 
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[36m(train_cnn_ray_tune pid=3059)[0m 
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[36m(train_cnn_ray_tune pid=3023)[0m 
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[36m(train_cnn_ray_tune pid=3023)[0m 
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[36m(train_cnn_ray_tune pid=3023)[0m Epoch 11/24[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=3066)[0m 
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[36m(train_cnn_ray_tune pid=3057)[0m 
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[36m(train_cnn_ray_tune pid=3057)[0m 
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[36m(train_cnn_ray_tune pid=3029)[0m 
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[36m(train_cnn_ray_tune pid=3064)[0m 
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[36m(train_cnn_ray_tune pid=3066)[0m 
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[36m(train_cnn_ray_tune pid=3051)[0m 
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[36m(train_cnn_ray_tune pid=3057)[0m 
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[36m(train_cnn_ray_tune pid=3053)[0m 
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[36m(train_cnn_ray_tune pid=3023)[0m 
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[36m(train_cnn_ray_tune pid=3054)[0m 
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[36m(train_cnn_ray_tune pid=3062)[0m 
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[36m(train_cnn_ray_tune pid=3062)[0m Epoch 10/25[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=3023)[0m 
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[36m(train_cnn_ray_tune pid=3058)[0m 
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[36m(train_cnn_ray_tune pid=3060)[0m 
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[36m(train_cnn_ray_tune pid=3052)[0m 
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[36m(train_cnn_ray_tune pid=3051)[0m 
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[36m(train_cnn_ray_tune pid=3064)[0m 
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[36m(train_cnn_ray_tune pid=3064)[0m 
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[36m(train_cnn_ray_tune pid=3066)[0m 
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[36m(train_cnn_ray_tune pid=3055)[0m 
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[36m(train_cnn_ray_tune pid=3062)[0m 
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[36m(train_cnn_ray_tune pid=3065)[0m 
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[36m(train_cnn_ray_tune pid=3043)[0m 
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[36m(train_cnn_ray_tune pid=3022)[0m 
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[36m(train_cnn_ray_tune pid=3022)[0m Epoch 10/24[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=3023)[0m 
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[36m(train_cnn_ray_tune pid=3065)[0m 
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[36m(train_cnn_ray_tune pid=3061)[0m 
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[36m(train_cnn_ray_tune pid=3064)[0m 
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[36m(train_cnn_ray_tune pid=3064)[0m 
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[36m(train_cnn_ray_tune pid=3052)[0m 
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[36m(train_cnn_ray_tune pid=3029)[0m 
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[36m(train_cnn_ray_tune pid=3059)[0m 
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[36m(train_cnn_ray_tune pid=3066)[0m 
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[36m(train_cnn_ray_tune pid=3043)[0m 
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[36m(train_cnn_ray_tune pid=3043)[0m 
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[36m(train_cnn_ray_tune pid=3043)[0m Epoch 5/19[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3066)[0m 
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[36m(train_cnn_ray_tune pid=3044)[0m 
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[36m(train_cnn_ray_tune pid=3029)[0m 
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[36m(train_cnn_ray_tune pid=3054)[0m 
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[36m(train_cnn_ray_tune pid=3062)[0m 
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[36m(train_cnn_ray_tune pid=3043)[0m 
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[36m(train_cnn_ray_tune pid=3061)[0m 
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[36m(train_cnn_ray_tune pid=3063)[0m 
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[36m(train_cnn_ray_tune pid=3022)[0m 
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[36m(train_cnn_ray_tune pid=3051)[0m 
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[36m(train_cnn_ray_tune pid=3051)[0m Epoch 12/16[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=3062)[0m 
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[36m(train_cnn_ray_tune pid=3023)[0m 
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[36m(train_cnn_ray_tune pid=3053)[0m 
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[36m(train_cnn_ray_tune pid=3062)[0m 
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[36m(train_cnn_ray_tune pid=3023)[0m 
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Trial status: 20 RUNNING
Current time: 2025-11-08 10:03:25. Total running time: 2min 30s
Logical resource usage: 20.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status       N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     num_resblocks     tasa_aprendizaje     epochs │
├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_6f442    RUNNING            2   rmsprop         relu                                   32                 64                  3                 0          6.72635e-06         24 │
│ trial_6f442    RUNNING            2   adam            tanh                                   32                 32                  3                 0          0.000114484         16 │
│ trial_6f442    RUNNING            3   adam            tanh                                   32                 64                  5                 1          7.68749e-05         27 │
│ trial_6f442    RUNNING            3   rmsprop         relu                                   32                 32                  5                 1          9.37956e-06         17 │
│ trial_6f442    RUNNING            2   adam            relu                                   32                 32                  3                 1          8.70224e-06         15 │
│ trial_6f442    RUNNING            2   rmsprop         tanh                                   32                 16                  5                 0          2.22726e-05         22 │
│ trial_6f442    RUNNING            3   rmsprop         relu                                   16                 16                  3                 0          0.000196867         23 │
│ trial_6f442    RUNNING            3   adam            tanh                                   16                 64                  3                 1          1.25914e-05         23 │
│ trial_6f442    RUNNING            2   adam            tanh                                   16                 16                  5                 1          5.51628e-05         28 │
│ trial_6f442    RUNNING            2   rmsprop         relu                                   16                 16                  5                 1          0.000135398         18 │
│ trial_6f442    RUNNING            3   adam            relu                                   16                 16                  3                 1          1.37357e-05         27 │
│ trial_6f442    RUNNING            2   adam            relu                                   32                 32                  5                 1          1.00068e-05         24 │
│ trial_6f442    RUNNING            2   rmsprop         tanh                                   16                 64                  5                 1          0.00018307          18 │
│ trial_6f442    RUNNING            3   adam            relu                                   16                 32                  3                 1          9.69082e-05         19 │
│ trial_6f442    RUNNING            3   rmsprop         tanh                                   32                 16                  5                 0          8.77002e-06         18 │
│ trial_6f442    RUNNING            2   adam            tanh                                   16                 16                  5                 0          5.34611e-06         27 │
│ trial_6f442    RUNNING            2   rmsprop         tanh                                   16                 16                  5                 0          2.27149e-05         28 │
│ trial_6f442    RUNNING            2   rmsprop         relu                                   32                 16                  3                 0          1.65226e-05         24 │
│ trial_6f442    RUNNING            2   adam            tanh                                   32                 64                  3                 0          5.15328e-05         21 │
│ trial_6f442    RUNNING            2   adam            relu                                   32                 64                  3                 1          0.000128258         25 │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3055)[0m 
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[36m(train_cnn_ray_tune pid=3022)[0m 
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[36m(train_cnn_ray_tune pid=3063)[0m 
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[36m(train_cnn_ray_tune pid=3053)[0m 
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[36m(train_cnn_ray_tune pid=3066)[0m 
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[36m(train_cnn_ray_tune pid=3062)[0m 
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[36m(train_cnn_ray_tune pid=3061)[0m 
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[36m(train_cnn_ray_tune pid=3052)[0m 
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[36m(train_cnn_ray_tune pid=3052)[0m Epoch 9/17[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=3059)[0m 
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[36m(train_cnn_ray_tune pid=3052)[0m 
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[36m(train_cnn_ray_tune pid=3065)[0m 
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[36m(train_cnn_ray_tune pid=3029)[0m 
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[36m(train_cnn_ray_tune pid=3029)[0m 
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[36m(train_cnn_ray_tune pid=3064)[0m 
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[36m(train_cnn_ray_tune pid=3052)[0m 
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[36m(train_cnn_ray_tune pid=3064)[0m 
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[36m(train_cnn_ray_tune pid=3059)[0m 
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[36m(train_cnn_ray_tune pid=3066)[0m 
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[36m(train_cnn_ray_tune pid=3023)[0m 
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[36m(train_cnn_ray_tune pid=3064)[0m 
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[36m(train_cnn_ray_tune pid=3064)[0m Epoch 8/27[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3066)[0m 
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[36m(train_cnn_ray_tune pid=3063)[0m 
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[36m(train_cnn_ray_tune pid=3053)[0m 
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[36m(train_cnn_ray_tune pid=3043)[0m 
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[36m(train_cnn_ray_tune pid=3029)[0m 
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[36m(train_cnn_ray_tune pid=3061)[0m 
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[36m(train_cnn_ray_tune pid=3043)[0m 
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[36m(train_cnn_ray_tune pid=3066)[0m 
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[36m(train_cnn_ray_tune pid=3065)[0m 
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[36m(train_cnn_ray_tune pid=3059)[0m 
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[36m(train_cnn_ray_tune pid=3059)[0m Epoch 13/15[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=3053)[0m 
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[36m(train_cnn_ray_tune pid=3023)[0m 
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[36m(train_cnn_ray_tune pid=3053)[0m 
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[36m(train_cnn_ray_tune pid=3044)[0m 
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[36m(train_cnn_ray_tune pid=3059)[0m 
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[36m(train_cnn_ray_tune pid=3062)[0m 
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[36m(train_cnn_ray_tune pid=3023)[0m 
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[36m(train_cnn_ray_tune pid=3061)[0m 
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[36m(train_cnn_ray_tune pid=3061)[0m 
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[36m(train_cnn_ray_tune pid=3061)[0m Epoch 11/18[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3062)[0m 
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[36m(train_cnn_ray_tune pid=3064)[0m 
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[36m(train_cnn_ray_tune pid=3052)[0m 
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[36m(train_cnn_ray_tune pid=3023)[0m 
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[36m(train_cnn_ray_tune pid=3059)[0m 
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[36m(train_cnn_ray_tune pid=3066)[0m 
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[36m(train_cnn_ray_tune pid=3054)[0m 
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[36m(train_cnn_ray_tune pid=3059)[0m 
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[36m(train_cnn_ray_tune pid=3054)[0m 
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[36m(train_cnn_ray_tune pid=3051)[0m 
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[36m(train_cnn_ray_tune pid=3051)[0m 
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[36m(train_cnn_ray_tune pid=3051)[0m Epoch 14/16[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=3029)[0m 
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[36m(train_cnn_ray_tune pid=3051)[0m 
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[36m(train_cnn_ray_tune pid=3053)[0m 
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[36m(train_cnn_ray_tune pid=3053)[0m 
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[36m(train_cnn_ray_tune pid=3051)[0m 
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[36m(train_cnn_ray_tune pid=3052)[0m 
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[36m(train_cnn_ray_tune pid=3062)[0m 
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[36m(train_cnn_ray_tune pid=3023)[0m 
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[36m(train_cnn_ray_tune pid=3058)[0m 
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[36m(train_cnn_ray_tune pid=3058)[0m 
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[36m(train_cnn_ray_tune pid=3058)[0m Epoch 15/22[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=3022)[0m 
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[36m(train_cnn_ray_tune pid=3052)[0m 
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Trial status: 20 RUNNING
Current time: 2025-11-08 10:03:55. Total running time: 3min 0s
Logical resource usage: 20.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status       N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     num_resblocks     tasa_aprendizaje     epochs │
├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_6f442    RUNNING            2   rmsprop         relu                                   32                 64                  3                 0          6.72635e-06         24 │
│ trial_6f442    RUNNING            2   adam            tanh                                   32                 32                  3                 0          0.000114484         16 │
│ trial_6f442    RUNNING            3   adam            tanh                                   32                 64                  5                 1          7.68749e-05         27 │
│ trial_6f442    RUNNING            3   rmsprop         relu                                   32                 32                  5                 1          9.37956e-06         17 │
│ trial_6f442    RUNNING            2   adam            relu                                   32                 32                  3                 1          8.70224e-06         15 │
│ trial_6f442    RUNNING            2   rmsprop         tanh                                   32                 16                  5                 0          2.22726e-05         22 │
│ trial_6f442    RUNNING            3   rmsprop         relu                                   16                 16                  3                 0          0.000196867         23 │
│ trial_6f442    RUNNING            3   adam            tanh                                   16                 64                  3                 1          1.25914e-05         23 │
│ trial_6f442    RUNNING            2   adam            tanh                                   16                 16                  5                 1          5.51628e-05         28 │
│ trial_6f442    RUNNING            2   rmsprop         relu                                   16                 16                  5                 1          0.000135398         18 │
│ trial_6f442    RUNNING            3   adam            relu                                   16                 16                  3                 1          1.37357e-05         27 │
│ trial_6f442    RUNNING            2   adam            relu                                   32                 32                  5                 1          1.00068e-05         24 │
│ trial_6f442    RUNNING            2   rmsprop         tanh                                   16                 64                  5                 1          0.00018307          18 │
│ trial_6f442    RUNNING            3   adam            relu                                   16                 32                  3                 1          9.69082e-05         19 │
│ trial_6f442    RUNNING            3   rmsprop         tanh                                   32                 16                  5                 0          8.77002e-06         18 │
│ trial_6f442    RUNNING            2   adam            tanh                                   16                 16                  5                 0          5.34611e-06         27 │
│ trial_6f442    RUNNING            2   rmsprop         tanh                                   16                 16                  5                 0          2.27149e-05         28 │
│ trial_6f442    RUNNING            2   rmsprop         relu                                   32                 16                  3                 0          1.65226e-05         24 │
│ trial_6f442    RUNNING            2   adam            tanh                                   32                 64                  3                 0          5.15328e-05         21 │
│ trial_6f442    RUNNING            2   adam            relu                                   32                 64                  3                 1          0.000128258         25 │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3062)[0m 
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[36m(train_cnn_ray_tune pid=3062)[0m 
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[36m(train_cnn_ray_tune pid=3059)[0m 
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[36m(train_cnn_ray_tune pid=3064)[0m 
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[36m(train_cnn_ray_tune pid=3059)[0m 
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[36m(train_cnn_ray_tune pid=3066)[0m 
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[36m(train_cnn_ray_tune pid=3023)[0m 
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[36m(train_cnn_ray_tune pid=3023)[0m 
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[36m(train_cnn_ray_tune pid=3023)[0m Epoch 16/24[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3062)[0m 
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[36m(train_cnn_ray_tune pid=3061)[0m 
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[36m(train_cnn_ray_tune pid=3022)[0m 
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[36m(train_cnn_ray_tune pid=3051)[0m 
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[36m(train_cnn_ray_tune pid=3043)[0m 
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[36m(train_cnn_ray_tune pid=3053)[0m 
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[36m(train_cnn_ray_tune pid=3029)[0m 
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[36m(train_cnn_ray_tune pid=3063)[0m 
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[36m(train_cnn_ray_tune pid=3023)[0m 
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[36m(train_cnn_ray_tune pid=3044)[0m 
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[36m(train_cnn_ray_tune pid=3065)[0m 
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[36m(train_cnn_ray_tune pid=3065)[0m Epoch 15/24[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=3059)[0m 
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[36m(train_cnn_ray_tune pid=3057)[0m 
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[36m(train_cnn_ray_tune pid=3052)[0m 
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[36m(train_cnn_ray_tune pid=3055)[0m 
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[36m(train_cnn_ray_tune pid=3066)[0m 
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[36m(train_cnn_ray_tune pid=3059)[0m 
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[36m(train_cnn_ray_tune pid=3055)[0m 
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[36m(train_cnn_ray_tune pid=3055)[0m 
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[36m(train_cnn_ray_tune pid=3062)[0m 
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[36m(train_cnn_ray_tune pid=3022)[0m 
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[36m(train_cnn_ray_tune pid=3063)[0m 
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[36m(train_cnn_ray_tune pid=3022)[0m 
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[36m(train_cnn_ray_tune pid=3022)[0m Epoch 14/24[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=3060)[0m 
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[36m(train_cnn_ray_tune pid=3062)[0m 
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[36m(train_cnn_ray_tune pid=3023)[0m 
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[36m(train_cnn_ray_tune pid=3060)[0m 
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[36m(train_cnn_ray_tune pid=3066)[0m 
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[36m(train_cnn_ray_tune pid=3060)[0m 
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[36m(train_cnn_ray_tune pid=3055)[0m 
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[36m(train_cnn_ray_tune pid=3023)[0m 
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[36m(train_cnn_ray_tune pid=3064)[0m 
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[36m(train_cnn_ray_tune pid=3064)[0m Epoch 10/27[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3051)[0m 
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[36m(train_cnn_ray_tune pid=3063)[0m 
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[36m(train_cnn_ray_tune pid=3055)[0m 
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[36m(train_cnn_ray_tune pid=3058)[0m 
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[36m(train_cnn_ray_tune pid=3055)[0m 
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[36m(train_cnn_ray_tune pid=3066)[0m 
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[36m(train_cnn_ray_tune pid=3059)[0m 
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[36m(train_cnn_ray_tune pid=3059)[0m 
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[36m(train_cnn_ray_tune pid=3064)[0m 
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[36m(train_cnn_ray_tune pid=3044)[0m 
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[36m(train_cnn_ray_tune pid=3063)[0m 
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[36m(train_cnn_ray_tune pid=3059)[0m 
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[36m(train_cnn_ray_tune pid=3059)[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=3059)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=3062)[0m 2025-11-08 10:00:58.891770: 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=3062)[0m 2025-11-08 10:00:58.914478: 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=3062)[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=3062)[0m E0000 00:00:1762592458.942960    4452 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=3062)[0m E0000 00:00:1762592458.951520    4452 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=3062)[0m W0000 00:00:1762592458.972154    4452 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=3062)[0m 2025-11-08 10:00:58.978266: 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=3062)[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=3064)[0m 2025-11-08 10:01:02.351990: 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=3064)[0m 2025-11-08 10:01:02.352040: 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=3064)[0m 2025-11-08 10:01:02.352048: 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=3064)[0m 2025-11-08 10:01:02.352052: 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=3064)[0m 2025-11-08 10:01:02.352056: 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=3064)[0m 2025-11-08 10:01:02.352059: 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=3064)[0m 2025-11-08 10:01:02.352341: 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=3064)[0m 2025-11-08 10:01:02.352383: 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=3064)[0m 2025-11-08 10:01:02.352386: 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=3059)[0m 
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[36m(train_cnn_ray_tune pid=3051)[0m 
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[36m(train_cnn_ray_tune pid=3059)[0m 
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[36m(train_cnn_ray_tune pid=3059)[0m 
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[36m(train_cnn_ray_tune pid=3059)[0m 
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[36m(train_cnn_ray_tune pid=3059)[0m 
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[36m(train_cnn_ray_tune pid=3059)[0m 
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[36m(train_cnn_ray_tune pid=3059)[0m 
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[36m(train_cnn_ray_tune pid=3059)[0m 
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[36m(train_cnn_ray_tune pid=3059)[0m 
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[36m(train_cnn_ray_tune pid=3059)[0m 
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[36m(train_cnn_ray_tune pid=3054)[0m 
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[36m(train_cnn_ray_tune pid=3054)[0m Epoch 8/23[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=3059)[0m 
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[36m(train_cnn_ray_tune pid=3061)[0m 
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[36m(train_cnn_ray_tune pid=3059)[0m 
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Trial trial_6f442 finished iteration 1 at 2025-11-08 10:04:20. Total running time: 3min 24s
╭──────────────────────────────────────╮
│ Trial trial_6f442 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             201.797 │
│ time_total_s                 201.797 │
│ training_iteration                 1 │
│ val_accuracy                 0.40976 │
╰──────────────────────────────────────╯

Trial trial_6f442 completed after 1 iterations at 2025-11-08 10:04:20. Total running time: 3min 24s
[36m(train_cnn_ray_tune pid=3023)[0m 
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[36m(train_cnn_ray_tune pid=3053)[0m 
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[36m(train_cnn_ray_tune pid=3065)[0m 
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[36m(train_cnn_ray_tune pid=3066)[0m 
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[36m(train_cnn_ray_tune pid=3022)[0m 
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[36m(train_cnn_ray_tune pid=3063)[0m 
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[36m(train_cnn_ray_tune pid=3065)[0m 
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[36m(train_cnn_ray_tune pid=3022)[0m 
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[36m(train_cnn_ray_tune pid=3023)[0m 
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[36m(train_cnn_ray_tune pid=3062)[0m 
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[36m(train_cnn_ray_tune pid=3062)[0m Epoch 15/25[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3052)[0m 
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Trial status: 19 RUNNING | 1 TERMINATED
Current time: 2025-11-08 10:04:25. Total running time: 3min 30s
Logical resource usage: 19.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     num_resblocks     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_6f442    RUNNING              2   rmsprop         relu                                   32                 64                  3                 0          6.72635e-06         24                                              │
│ trial_6f442    RUNNING              2   adam            tanh                                   32                 32                  3                 0          0.000114484         16                                              │
│ trial_6f442    RUNNING              3   adam            tanh                                   32                 64                  5                 1          7.68749e-05         27                                              │
│ trial_6f442    RUNNING              3   rmsprop         relu                                   32                 32                  5                 1          9.37956e-06         17                                              │
│ trial_6f442    RUNNING              2   rmsprop         tanh                                   32                 16                  5                 0          2.22726e-05         22                                              │
│ trial_6f442    RUNNING              3   rmsprop         relu                                   16                 16                  3                 0          0.000196867         23                                              │
│ trial_6f442    RUNNING              3   adam            tanh                                   16                 64                  3                 1          1.25914e-05         23                                              │
│ trial_6f442    RUNNING              2   adam            tanh                                   16                 16                  5                 1          5.51628e-05         28                                              │
│ trial_6f442    RUNNING              2   rmsprop         relu                                   16                 16                  5                 1          0.000135398         18                                              │
│ trial_6f442    RUNNING              3   adam            relu                                   16                 16                  3                 1          1.37357e-05         27                                              │
│ trial_6f442    RUNNING              2   adam            relu                                   32                 32                  5                 1          1.00068e-05         24                                              │
│ trial_6f442    RUNNING              2   rmsprop         tanh                                   16                 64                  5                 1          0.00018307          18                                              │
│ trial_6f442    RUNNING              3   adam            relu                                   16                 32                  3                 1          9.69082e-05         19                                              │
│ trial_6f442    RUNNING              3   rmsprop         tanh                                   32                 16                  5                 0          8.77002e-06         18                                              │
│ trial_6f442    RUNNING              2   adam            tanh                                   16                 16                  5                 0          5.34611e-06         27                                              │
│ trial_6f442    RUNNING              2   rmsprop         tanh                                   16                 16                  5                 0          2.27149e-05         28                                              │
│ trial_6f442    RUNNING              2   rmsprop         relu                                   32                 16                  3                 0          1.65226e-05         24                                              │
│ trial_6f442    RUNNING              2   adam            tanh                                   32                 64                  3                 0          5.15328e-05         21                                              │
│ trial_6f442    RUNNING              2   adam            relu                                   32                 64                  3                 1          0.000128258         25                                              │
│ trial_6f442    TERMINATED           2   adam            relu                                   32                 32                  3                 1          8.70224e-06         15        1            201.797         0.409761 │
╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3051)[0m 
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[36m(train_cnn_ray_tune pid=3066)[0m 
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[36m(train_cnn_ray_tune pid=3051)[0m 
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[36m(train_cnn_ray_tune pid=3051)[0m 
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[36m(train_cnn_ray_tune pid=3051)[0m 
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[1m19/49[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 18ms/step
[36m(train_cnn_ray_tune pid=3057)[0m 
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[36m(train_cnn_ray_tune pid=3051)[0m 
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[1m27/49[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 17ms/step
[36m(train_cnn_ray_tune pid=3044)[0m 
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[36m(train_cnn_ray_tune pid=3054)[0m 
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[36m(train_cnn_ray_tune pid=3061)[0m 
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[36m(train_cnn_ray_tune pid=3051)[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=3051)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=3051)[0m 
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Trial trial_6f442 finished iteration 1 at 2025-11-08 10:04:29. Total running time: 3min 34s
╭──────────────────────────────────────╮
│ Trial trial_6f442 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             211.277 │
│ time_total_s                 211.277 │
│ training_iteration                 1 │
│ val_accuracy                 0.49263 │
╰──────────────────────────────────────╯

Trial trial_6f442 completed after 1 iterations at 2025-11-08 10:04:29. Total running time: 3min 34s
[36m(train_cnn_ray_tune pid=3065)[0m 
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[36m(train_cnn_ray_tune pid=3065)[0m Epoch 17/24[32m [repeated 9x across cluster][0m
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[36m(train_cnn_ray_tune pid=3057)[0m 
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[36m(train_cnn_ray_tune pid=3060)[0m 
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[36m(train_cnn_ray_tune pid=3061)[0m 
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[36m(train_cnn_ray_tune pid=3054)[0m 
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[36m(train_cnn_ray_tune pid=3029)[0m 
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[36m(train_cnn_ray_tune pid=3066)[0m 
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[36m(train_cnn_ray_tune pid=3044)[0m 
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[36m(train_cnn_ray_tune pid=3053)[0m 
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[36m(train_cnn_ray_tune pid=3053)[0m 
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[36m(train_cnn_ray_tune pid=3053)[0m Epoch 16/21[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=3066)[0m 
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[36m(train_cnn_ray_tune pid=3062)[0m 
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[36m(train_cnn_ray_tune pid=3029)[0m 
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[36m(train_cnn_ray_tune pid=3066)[0m 
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[36m(train_cnn_ray_tune pid=3064)[0m 
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[36m(train_cnn_ray_tune pid=3053)[0m 
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[36m(train_cnn_ray_tune pid=3062)[0m 
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[36m(train_cnn_ray_tune pid=3058)[0m 
[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m28s[0m 87ms/step - accuracy: 0.4062 - loss: 1.3652
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[36m(train_cnn_ray_tune pid=3058)[0m 
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m11s[0m 33ms/step - accuracy: 0.3537 - loss: 1.3877 - val_accuracy: 0.3423 - val_loss: 1.3282[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3058)[0m Epoch 19/22[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3065)[0m 
[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m26s[0m 81ms/step - accuracy: 0.4062 - loss: 1.5554
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[36m(train_cnn_ray_tune pid=3064)[0m 
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[36m(train_cnn_ray_tune pid=3023)[0m 
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[36m(train_cnn_ray_tune pid=3023)[0m 
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[36m(train_cnn_ray_tune pid=3023)[0m 
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[36m(train_cnn_ray_tune pid=3063)[0m 
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[36m(train_cnn_ray_tune pid=3029)[0m 
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[36m(train_cnn_ray_tune pid=3063)[0m 
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[36m(train_cnn_ray_tune pid=3029)[0m 
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[36m(train_cnn_ray_tune pid=3043)[0m 
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[36m(train_cnn_ray_tune pid=3066)[0m 
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[36m(train_cnn_ray_tune pid=3066)[0m Epoch 11/28[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=3044)[0m 
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[36m(train_cnn_ray_tune pid=3062)[0m 
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[36m(train_cnn_ray_tune pid=3022)[0m 
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[36m(train_cnn_ray_tune pid=3061)[0m 
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[36m(train_cnn_ray_tune pid=3066)[0m 
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[36m(train_cnn_ray_tune pid=3029)[0m 
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[36m(train_cnn_ray_tune pid=3060)[0m 
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[36m(train_cnn_ray_tune pid=3023)[0m 
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[36m(train_cnn_ray_tune pid=3022)[0m 
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[36m(train_cnn_ray_tune pid=3057)[0m 
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[36m(train_cnn_ray_tune pid=3062)[0m 
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[36m(train_cnn_ray_tune pid=3062)[0m 
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[36m(train_cnn_ray_tune pid=3062)[0m 
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[36m(train_cnn_ray_tune pid=3062)[0m 
[1m18/49[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 12ms/step
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[36m(train_cnn_ray_tune pid=3023)[0m 
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[36m(train_cnn_ray_tune pid=3062)[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=3062)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=3062)[0m 
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[36m(train_cnn_ray_tune pid=3062)[0m 
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[36m(train_cnn_ray_tune pid=3058)[0m Epoch 20/22[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3022)[0m 
[1m  3/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 31ms/step - accuracy: 0.3507 - loss: 1.4622 
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[36m(train_cnn_ray_tune pid=3062)[0m 
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[36m(train_cnn_ray_tune pid=3062)[0m 
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[36m(train_cnn_ray_tune pid=3062)[0m 
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[36m(train_cnn_ray_tune pid=3062)[0m 
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[36m(train_cnn_ray_tune pid=3062)[0m 
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[36m(train_cnn_ray_tune pid=3062)[0m 
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[36m(train_cnn_ray_tune pid=3062)[0m 
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[36m(train_cnn_ray_tune pid=3062)[0m 
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[36m(train_cnn_ray_tune pid=3062)[0m 
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Trial trial_6f442 finished iteration 1 at 2025-11-08 10:04:51. Total running time: 3min 56s
╭──────────────────────────────────────╮
│ Trial trial_6f442 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             233.065 │
│ time_total_s                 233.065 │
│ training_iteration                 1 │
│ val_accuracy                 0.55232 │
╰──────────────────────────────────────╯

Trial trial_6f442 completed after 1 iterations at 2025-11-08 10:04:51. Total running time: 3min 56s
[36m(train_cnn_ray_tune pid=3062)[0m 
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[36m(train_cnn_ray_tune pid=3065)[0m 
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[36m(train_cnn_ray_tune pid=3064)[0m 
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[36m(train_cnn_ray_tune pid=3065)[0m 
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[36m(train_cnn_ray_tune pid=3054)[0m 
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[36m(train_cnn_ray_tune pid=3058)[0m 
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[36m(train_cnn_ray_tune pid=3029)[0m 
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[36m(train_cnn_ray_tune pid=3044)[0m 
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[36m(train_cnn_ray_tune pid=3066)[0m 
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[36m(train_cnn_ray_tune pid=3029)[0m 
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[36m(train_cnn_ray_tune pid=3029)[0m Epoch 9/27[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=3064)[0m 
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[36m(train_cnn_ray_tune pid=3043)[0m 
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Trial status: 17 RUNNING | 3 TERMINATED
Current time: 2025-11-08 10:04:56. Total running time: 4min 0s
Logical resource usage: 17.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     num_resblocks     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_6f442    RUNNING              2   rmsprop         relu                                   32                 64                  3                 0          6.72635e-06         24                                              │
│ trial_6f442    RUNNING              3   adam            tanh                                   32                 64                  5                 1          7.68749e-05         27                                              │
│ trial_6f442    RUNNING              3   rmsprop         relu                                   32                 32                  5                 1          9.37956e-06         17                                              │
│ trial_6f442    RUNNING              2   rmsprop         tanh                                   32                 16                  5                 0          2.22726e-05         22                                              │
│ trial_6f442    RUNNING              3   rmsprop         relu                                   16                 16                  3                 0          0.000196867         23                                              │
│ trial_6f442    RUNNING              3   adam            tanh                                   16                 64                  3                 1          1.25914e-05         23                                              │
│ trial_6f442    RUNNING              2   adam            tanh                                   16                 16                  5                 1          5.51628e-05         28                                              │
│ trial_6f442    RUNNING              2   rmsprop         relu                                   16                 16                  5                 1          0.000135398         18                                              │
│ trial_6f442    RUNNING              3   adam            relu                                   16                 16                  3                 1          1.37357e-05         27                                              │
│ trial_6f442    RUNNING              2   adam            relu                                   32                 32                  5                 1          1.00068e-05         24                                              │
│ trial_6f442    RUNNING              2   rmsprop         tanh                                   16                 64                  5                 1          0.00018307          18                                              │
│ trial_6f442    RUNNING              3   adam            relu                                   16                 32                  3                 1          9.69082e-05         19                                              │
│ trial_6f442    RUNNING              3   rmsprop         tanh                                   32                 16                  5                 0          8.77002e-06         18                                              │
│ trial_6f442    RUNNING              2   adam            tanh                                   16                 16                  5                 0          5.34611e-06         27                                              │
│ trial_6f442    RUNNING              2   rmsprop         tanh                                   16                 16                  5                 0          2.27149e-05         28                                              │
│ trial_6f442    RUNNING              2   rmsprop         relu                                   32                 16                  3                 0          1.65226e-05         24                                              │
│ trial_6f442    RUNNING              2   adam            tanh                                   32                 64                  3                 0          5.15328e-05         21                                              │
│ trial_6f442    TERMINATED           2   adam            tanh                                   32                 32                  3                 0          0.000114484         16        1            211.277         0.492626 │
│ trial_6f442    TERMINATED           2   adam            relu                                   32                 32                  3                 1          8.70224e-06         15        1            201.797         0.409761 │
│ trial_6f442    TERMINATED           2   adam            relu                                   32                 64                  3                 1          0.000128258         25        1            233.065         0.552317 │
╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3023)[0m 
[1m199/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m3s[0m 26ms/step - accuracy: 0.3815 - loss: 1.3882
[1m201/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m3s[0m 26ms/step - accuracy: 0.3814 - loss: 1.3883
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[36m(train_cnn_ray_tune pid=3052)[0m 
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[36m(train_cnn_ray_tune pid=3043)[0m 
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[36m(train_cnn_ray_tune pid=3054)[0m 
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[36m(train_cnn_ray_tune pid=3029)[0m 
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[36m(train_cnn_ray_tune pid=3066)[0m 
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[36m(train_cnn_ray_tune pid=3052)[0m 
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[36m(train_cnn_ray_tune pid=3058)[0m 
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[36m(train_cnn_ray_tune pid=3022)[0m 
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[36m(train_cnn_ray_tune pid=3022)[0m Epoch 18/24[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3060)[0m 
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[36m(train_cnn_ray_tune pid=3066)[0m 
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[36m(train_cnn_ray_tune pid=3061)[0m 
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[36m(train_cnn_ray_tune pid=3029)[0m 
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[36m(train_cnn_ray_tune pid=3023)[0m 
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[36m(train_cnn_ray_tune pid=3063)[0m 
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[36m(train_cnn_ray_tune pid=3063)[0m 
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[36m(train_cnn_ray_tune pid=3065)[0m 
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[36m(train_cnn_ray_tune pid=3063)[0m 
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[36m(train_cnn_ray_tune pid=3063)[0m 
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[36m(train_cnn_ray_tune pid=3063)[0m 
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[36m(train_cnn_ray_tune pid=3063)[0m 
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[36m(train_cnn_ray_tune pid=3063)[0m 
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[36m(train_cnn_ray_tune pid=3063)[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=3063)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=3064)[0m 
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[36m(train_cnn_ray_tune pid=3055)[0m 
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[36m(train_cnn_ray_tune pid=3055)[0m Epoch 13/28[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=3063)[0m 
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[36m(train_cnn_ray_tune pid=3063)[0m 
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[36m(train_cnn_ray_tune pid=3063)[0m 
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[36m(train_cnn_ray_tune pid=3063)[0m 
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Trial trial_6f442 finished iteration 1 at 2025-11-08 10:05:06. Total running time: 4min 11s
╭──────────────────────────────────────╮
│ Trial trial_6f442 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             247.801 │
│ time_total_s                 247.801 │
│ training_iteration                 1 │
│ val_accuracy                 0.45295 │
╰──────────────────────────────────────╯

Trial trial_6f442 completed after 1 iterations at 2025-11-08 10:05:06. Total running time: 4min 11s
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[36m(train_cnn_ray_tune pid=3043)[0m 
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[36m(train_cnn_ray_tune pid=3057)[0m 
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[36m(train_cnn_ray_tune pid=3061)[0m 
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[36m(train_cnn_ray_tune pid=3022)[0m 
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[36m(train_cnn_ray_tune pid=3022)[0m Epoch 19/24[32m [repeated 6x across cluster][0m
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[36m(train_cnn_ray_tune pid=3023)[0m 
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[36m(train_cnn_ray_tune pid=3054)[0m 
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[36m(train_cnn_ray_tune pid=3064)[0m 
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[36m(train_cnn_ray_tune pid=3053)[0m 
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[36m(train_cnn_ray_tune pid=3065)[0m 
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[36m(train_cnn_ray_tune pid=3065)[0m 
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[36m(train_cnn_ray_tune pid=3065)[0m Epoch 21/24[32m [repeated 3x across cluster][0m
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[36m(train_cnn_ray_tune pid=3058)[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=3058)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=3058)[0m 
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[36m(train_cnn_ray_tune pid=3023)[0m 
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[36m(train_cnn_ray_tune pid=3023)[0m 
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[36m(train_cnn_ray_tune pid=3058)[0m 
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Trial trial_6f442 finished iteration 1 at 2025-11-08 10:05:18. Total running time: 4min 23s
╭──────────────────────────────────────╮
│ Trial trial_6f442 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             260.574 │
│ time_total_s                 260.574 │
│ training_iteration                 1 │
│ val_accuracy                 0.35183 │
╰──────────────────────────────────────╯

Trial trial_6f442 completed after 1 iterations at 2025-11-08 10:05:19. Total running time: 4min 23s
[36m(train_cnn_ray_tune pid=3055)[0m 
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[36m(train_cnn_ray_tune pid=3055)[0m Epoch 14/28[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=3055)[0m 
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[36m(train_cnn_ray_tune pid=3022)[0m 
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[36m(train_cnn_ray_tune pid=3029)[0m 
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[36m(train_cnn_ray_tune pid=3066)[0m 
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[36m(train_cnn_ray_tune pid=3044)[0m 
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[36m(train_cnn_ray_tune pid=3023)[0m 
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[36m(train_cnn_ray_tune pid=3064)[0m 
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[36m(train_cnn_ray_tune pid=3064)[0m Epoch 14/27[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=3065)[0m 
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[36m(train_cnn_ray_tune pid=3055)[0m 
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[36m(train_cnn_ray_tune pid=3061)[0m 
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Trial status: 15 RUNNING | 5 TERMINATED
Current time: 2025-11-08 10:05:26. Total running time: 4min 30s
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_6f442    RUNNING              2   rmsprop         relu                                   32                 64                  3                 0          6.72635e-06         24                                              │
│ trial_6f442    RUNNING              3   adam            tanh                                   32                 64                  5                 1          7.68749e-05         27                                              │
│ trial_6f442    RUNNING              3   rmsprop         relu                                   32                 32                  5                 1          9.37956e-06         17                                              │
│ trial_6f442    RUNNING              3   rmsprop         relu                                   16                 16                  3                 0          0.000196867         23                                              │
│ trial_6f442    RUNNING              3   adam            tanh                                   16                 64                  3                 1          1.25914e-05         23                                              │
│ trial_6f442    RUNNING              2   adam            tanh                                   16                 16                  5                 1          5.51628e-05         28                                              │
│ trial_6f442    RUNNING              2   rmsprop         relu                                   16                 16                  5                 1          0.000135398         18                                              │
│ trial_6f442    RUNNING              3   adam            relu                                   16                 16                  3                 1          1.37357e-05         27                                              │
│ trial_6f442    RUNNING              2   adam            relu                                   32                 32                  5                 1          1.00068e-05         24                                              │
│ trial_6f442    RUNNING              3   adam            relu                                   16                 32                  3                 1          9.69082e-05         19                                              │
│ trial_6f442    RUNNING              3   rmsprop         tanh                                   32                 16                  5                 0          8.77002e-06         18                                              │
│ trial_6f442    RUNNING              2   adam            tanh                                   16                 16                  5                 0          5.34611e-06         27                                              │
│ trial_6f442    RUNNING              2   rmsprop         tanh                                   16                 16                  5                 0          2.27149e-05         28                                              │
│ trial_6f442    RUNNING              2   rmsprop         relu                                   32                 16                  3                 0          1.65226e-05         24                                              │
│ trial_6f442    RUNNING              2   adam            tanh                                   32                 64                  3                 0          5.15328e-05         21                                              │
│ trial_6f442    TERMINATED           2   adam            tanh                                   32                 32                  3                 0          0.000114484         16        1            211.277         0.492626 │
│ trial_6f442    TERMINATED           2   adam            relu                                   32                 32                  3                 1          8.70224e-06         15        1            201.797         0.409761 │
│ trial_6f442    TERMINATED           2   rmsprop         tanh                                   32                 16                  5                 0          2.22726e-05         22        1            260.574         0.351826 │
│ trial_6f442    TERMINATED           2   rmsprop         tanh                                   16                 64                  5                 1          0.00018307          18        1            247.801         0.452949 │
│ trial_6f442    TERMINATED           2   adam            relu                                   32                 64                  3                 1          0.000128258         25        1            233.065         0.552317 │
╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3065)[0m 
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[36m(train_cnn_ray_tune pid=3052)[0m 
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[36m(train_cnn_ray_tune pid=3064)[0m 
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[36m(train_cnn_ray_tune pid=3029)[0m 
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[36m(train_cnn_ray_tune pid=3054)[0m 
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[36m(train_cnn_ray_tune pid=3023)[0m 
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[36m(train_cnn_ray_tune pid=3054)[0m 
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[36m(train_cnn_ray_tune pid=3054)[0m Epoch 11/23[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3023)[0m 
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[36m(train_cnn_ray_tune pid=3052)[0m 
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[36m(train_cnn_ray_tune pid=3023)[0m 
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[36m(train_cnn_ray_tune pid=3064)[0m 
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[36m(train_cnn_ray_tune pid=3065)[0m 
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[36m(train_cnn_ray_tune pid=3054)[0m 
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[36m(train_cnn_ray_tune pid=3043)[0m 
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[36m(train_cnn_ray_tune pid=3054)[0m 
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[36m(train_cnn_ray_tune pid=3029)[0m 
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[36m(train_cnn_ray_tune pid=3023)[0m 
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[36m(train_cnn_ray_tune pid=3023)[0m 
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[36m(train_cnn_ray_tune pid=3023)[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=3023)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=3044)[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=3044)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=3023)[0m 
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[36m(train_cnn_ray_tune pid=3023)[0m 
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[36m(train_cnn_ray_tune pid=3023)[0m 
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[36m(train_cnn_ray_tune pid=3023)[0m 
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[36m(train_cnn_ray_tune pid=3055)[0m 
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[36m(train_cnn_ray_tune pid=3055)[0m Epoch 15/28[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=3044)[0m 
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[36m(train_cnn_ray_tune pid=3023)[0m 
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[36m(train_cnn_ray_tune pid=3023)[0m 
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[36m(train_cnn_ray_tune pid=3023)[0m 
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Trial trial_6f442 finished iteration 1 at 2025-11-08 10:05:35. Total running time: 4min 39s
╭─────────────────────────────────────╮
│ Trial trial_6f442 result            │
├─────────────────────────────────────┤
│ checkpoint_dir_name                 │
│ time_this_iter_s              276.9 │
│ time_total_s                  276.9 │
│ training_iteration                1 │
│ val_accuracy                 0.4677 │
╰─────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3023)[0m 
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Trial trial_6f442 completed after 1 iterations at 2025-11-08 10:05:35. Total running time: 4min 39s
[36m(train_cnn_ray_tune pid=3061)[0m 
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[36m(train_cnn_ray_tune pid=3066)[0m 
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[36m(train_cnn_ray_tune pid=3061)[0m 
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Trial trial_6f442 finished iteration 1 at 2025-11-08 10:05:37. Total running time: 4min 41s
╭──────────────────────────────────────╮
│ Trial trial_6f442 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             278.832 │
│ time_total_s                 278.832 │
│ training_iteration                 1 │
│ val_accuracy                 0.44909 │
╰──────────────────────────────────────╯

Trial trial_6f442 completed after 1 iterations at 2025-11-08 10:05:37. Total running time: 4min 41s
[36m(train_cnn_ray_tune pid=3061)[0m 
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[36m(train_cnn_ray_tune pid=3066)[0m 
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[36m(train_cnn_ray_tune pid=3066)[0m 
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[36m(train_cnn_ray_tune pid=3061)[0m 
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Trial trial_6f442 finished iteration 1 at 2025-11-08 10:05:37. Total running time: 4min 42s
╭──────────────────────────────────────╮
│ Trial trial_6f442 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             279.442 │
│ time_total_s                 279.442 │
│ training_iteration                 1 │
│ val_accuracy                 0.35218 │
╰──────────────────────────────────────╯

Trial trial_6f442 completed after 1 iterations at 2025-11-08 10:05:37. Total running time: 4min 42s
[36m(train_cnn_ray_tune pid=3057)[0m 
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[36m(train_cnn_ray_tune pid=3055)[0m 
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[36m(train_cnn_ray_tune pid=3057)[0m 
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[36m(train_cnn_ray_tune pid=3029)[0m 
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[36m(train_cnn_ray_tune pid=3061)[0m 
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[36m(train_cnn_ray_tune pid=3053)[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=3053)[0m   _log_deprecation_warning([32m [repeated 2x across cluster][0m
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[36m(train_cnn_ray_tune pid=3064)[0m 
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Trial trial_6f442 finished iteration 1 at 2025-11-08 10:05:42. Total running time: 4min 46s
╭──────────────────────────────────────╮
│ Trial trial_6f442 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             283.823 │
│ time_total_s                 283.823 │
│ training_iteration                 1 │
│ val_accuracy                 0.48736 │
╰──────────────────────────────────────╯

Trial trial_6f442 completed after 1 iterations at 2025-11-08 10:05:42. Total running time: 4min 46s
[36m(train_cnn_ray_tune pid=3043)[0m 
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[36m(train_cnn_ray_tune pid=3065)[0m 
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[36m(train_cnn_ray_tune pid=3066)[0m 
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[36m(train_cnn_ray_tune pid=3043)[0m 
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[36m(train_cnn_ray_tune pid=3029)[0m 
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[36m(train_cnn_ray_tune pid=3043)[0m 
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[36m(train_cnn_ray_tune pid=3029)[0m 
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[36m(train_cnn_ray_tune pid=3065)[0m 
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[36m(train_cnn_ray_tune pid=3022)[0m Epoch 23/24[32m [repeated 6x across cluster][0m
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Trial trial_6f442 finished iteration 1 at 2025-11-08 10:05:46. Total running time: 4min 51s
╭──────────────────────────────────────╮
│ Trial trial_6f442 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             288.182 │
│ time_total_s                 288.182 │
│ training_iteration                 1 │
│ val_accuracy                 0.44663 │
╰──────────────────────────────────────╯

Trial trial_6f442 completed after 1 iterations at 2025-11-08 10:05:46. Total running time: 4min 51s
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[36m(train_cnn_ray_tune pid=3060)[0m 
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[36m(train_cnn_ray_tune pid=3022)[0m 
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[36m(train_cnn_ray_tune pid=3029)[0m 
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[36m(train_cnn_ray_tune pid=3057)[0m 
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[36m(train_cnn_ray_tune pid=3057)[0m 
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[36m(train_cnn_ray_tune pid=3066)[0m 
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[36m(train_cnn_ray_tune pid=3057)[0m 
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[36m(train_cnn_ray_tune pid=3057)[0m 
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[36m(train_cnn_ray_tune pid=3057)[0m 
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[36m(train_cnn_ray_tune pid=3057)[0m 
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[36m(train_cnn_ray_tune pid=3022)[0m 
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[36m(train_cnn_ray_tune pid=3022)[0m Epoch 24/24[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=3057)[0m 
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[36m(train_cnn_ray_tune pid=3057)[0m 
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[36m(train_cnn_ray_tune pid=3057)[0m 
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[36m(train_cnn_ray_tune pid=3043)[0m 
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[36m(train_cnn_ray_tune pid=3055)[0m 
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Trial trial_6f442 finished iteration 1 at 2025-11-08 10:05:54. Total running time: 4min 59s
[36m(train_cnn_ray_tune pid=3052)[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=3052)[0m   _log_deprecation_warning([32m [repeated 2x across cluster][0m
╭──────────────────────────────────────╮
│ Trial trial_6f442 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             296.348 │
│ time_total_s                 296.348 │
│ training_iteration                 1 │
│ val_accuracy                 0.48666 │
╰──────────────────────────────────────╯

Trial trial_6f442 completed after 1 iterations at 2025-11-08 10:05:54. Total running time: 4min 59s
[36m(train_cnn_ray_tune pid=3022)[0m 
[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 250ms/step
[36m(train_cnn_ray_tune pid=3022)[0m 
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[36m(train_cnn_ray_tune pid=3064)[0m Epoch 17/27[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=3022)[0m 
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Trial trial_6f442 finished iteration 1 at 2025-11-08 10:05:56. Total running time: 5min 0s
╭──────────────────────────────────────╮
│ Trial trial_6f442 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             297.759 │
│ time_total_s                 297.759 │
│ training_iteration                 1 │
│ val_accuracy                 0.47507 │
╰──────────────────────────────────────╯

Trial trial_6f442 completed after 1 iterations at 2025-11-08 10:05:56. Total running time: 5min 0s
[36m(train_cnn_ray_tune pid=3022)[0m 
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Trial status: 12 TERMINATED | 8 RUNNING
Current time: 2025-11-08 10:05:56. Total running time: 5min 0s
Logical resource usage: 8.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     num_resblocks     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_6f442    RUNNING              3   rmsprop         relu                                   16                 16                  3                 0          0.000196867         23                                              │
│ trial_6f442    RUNNING              3   adam            tanh                                   16                 64                  3                 1          1.25914e-05         23                                              │
│ trial_6f442    RUNNING              2   adam            tanh                                   16                 16                  5                 1          5.51628e-05         28                                              │
│ trial_6f442    RUNNING              2   rmsprop         relu                                   16                 16                  5                 1          0.000135398         18                                              │
│ trial_6f442    RUNNING              3   adam            relu                                   16                 16                  3                 1          1.37357e-05         27                                              │
│ trial_6f442    RUNNING              3   adam            relu                                   16                 32                  3                 1          9.69082e-05         19                                              │
│ trial_6f442    RUNNING              2   adam            tanh                                   16                 16                  5                 0          5.34611e-06         27                                              │
│ trial_6f442    RUNNING              2   rmsprop         tanh                                   16                 16                  5                 0          2.27149e-05         28                                              │
│ trial_6f442    TERMINATED           2   rmsprop         relu                                   32                 64                  3                 0          6.72635e-06         24        1            297.759         0.47507  │
│ trial_6f442    TERMINATED           2   adam            tanh                                   32                 32                  3                 0          0.000114484         16        1            211.277         0.492626 │
│ trial_6f442    TERMINATED           3   adam            tanh                                   32                 64                  5                 1          7.68749e-05         27        1            278.832         0.449087 │
│ trial_6f442    TERMINATED           3   rmsprop         relu                                   32                 32                  5                 1          9.37956e-06         17        1            296.348         0.486657 │
│ trial_6f442    TERMINATED           2   adam            relu                                   32                 32                  3                 1          8.70224e-06         15        1            201.797         0.409761 │
│ trial_6f442    TERMINATED           2   rmsprop         tanh                                   32                 16                  5                 0          2.22726e-05         22        1            260.574         0.351826 │
│ trial_6f442    TERMINATED           2   adam            relu                                   32                 32                  5                 1          1.00068e-05         24        1            288.182         0.446629 │
│ trial_6f442    TERMINATED           2   rmsprop         tanh                                   16                 64                  5                 1          0.00018307          18        1            247.801         0.452949 │
│ trial_6f442    TERMINATED           3   rmsprop         tanh                                   32                 16                  5                 0          8.77002e-06         18        1            279.442         0.352177 │
│ trial_6f442    TERMINATED           2   rmsprop         relu                                   32                 16                  3                 0          1.65226e-05         24        1            276.9           0.467697 │
│ trial_6f442    TERMINATED           2   adam            tanh                                   32                 64                  3                 0          5.15328e-05         21        1            283.823         0.48736  │
│ trial_6f442    TERMINATED           2   adam            relu                                   32                 64                  3                 1          0.000128258         25        1            233.065         0.552317 │
╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3064)[0m 
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[36m(train_cnn_ray_tune pid=3064)[0m 
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[36m(train_cnn_ray_tune pid=3064)[0m 
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[36m(train_cnn_ray_tune pid=3064)[0m 
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[36m(train_cnn_ray_tune pid=3064)[0m 
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[36m(train_cnn_ray_tune pid=3066)[0m 
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[36m(train_cnn_ray_tune pid=3064)[0m 
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[36m(train_cnn_ray_tune pid=3064)[0m 
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[36m(train_cnn_ray_tune pid=3064)[0m 
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[36m(train_cnn_ray_tune pid=3066)[0m 
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[36m(train_cnn_ray_tune pid=3060)[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=3060)[0m   _log_deprecation_warning([32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3043)[0m 
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[36m(train_cnn_ray_tune pid=3066)[0m Epoch 18/28[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3060)[0m 
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Trial trial_6f442 finished iteration 1 at 2025-11-08 10:06:05. Total running time: 5min 9s
╭──────────────────────────────────────╮
│ Trial trial_6f442 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             306.842 │
│ time_total_s                 306.842 │
│ training_iteration                 1 │
│ val_accuracy                 0.54354 │
╰──────────────────────────────────────╯

Trial trial_6f442 completed after 1 iterations at 2025-11-08 10:06:05. Total running time: 5min 9s
[36m(train_cnn_ray_tune pid=3064)[0m 
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[36m(train_cnn_ray_tune pid=3066)[0m 
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[36m(train_cnn_ray_tune pid=3029)[0m 
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[36m(train_cnn_ray_tune pid=3064)[0m 
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[36m(train_cnn_ray_tune pid=3055)[0m 
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[36m(train_cnn_ray_tune pid=3066)[0m 
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[36m(train_cnn_ray_tune pid=3054)[0m 
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[36m(train_cnn_ray_tune pid=3029)[0m 
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[36m(train_cnn_ray_tune pid=3054)[0m 
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[36m(train_cnn_ray_tune pid=3054)[0m 
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[36m(train_cnn_ray_tune pid=3066)[0m Epoch 19/28[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3055)[0m 
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[36m(train_cnn_ray_tune pid=3066)[0m 
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[36m(train_cnn_ray_tune pid=3064)[0m 
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[36m(train_cnn_ray_tune pid=3057)[0m 
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[36m(train_cnn_ray_tune pid=3043)[0m 
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[36m(train_cnn_ray_tune pid=3064)[0m 
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[36m(train_cnn_ray_tune pid=3055)[0m 
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[36m(train_cnn_ray_tune pid=3064)[0m 
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[36m(train_cnn_ray_tune pid=3043)[0m 
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[36m(train_cnn_ray_tune pid=3029)[0m 
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[36m(train_cnn_ray_tune pid=3043)[0m 
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[36m(train_cnn_ray_tune pid=3029)[0m 
[1m655/655[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 11ms/step - accuracy: 0.3806 - loss: 1.4154 - val_accuracy: 0.4003 - val_loss: 1.3324[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3066)[0m Epoch 20/28[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=3066)[0m 
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[36m(train_cnn_ray_tune pid=3066)[0m 
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[36m(train_cnn_ray_tune pid=3055)[0m 
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[36m(train_cnn_ray_tune pid=3054)[0m 
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[36m(train_cnn_ray_tune pid=3055)[0m 
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[36m(train_cnn_ray_tune pid=3064)[0m 
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[36m(train_cnn_ray_tune pid=3029)[0m 
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[36m(train_cnn_ray_tune pid=3029)[0m 
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[36m(train_cnn_ray_tune pid=3055)[0m 
[1m457/655[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m1s[0m 8ms/step - accuracy: 0.4234 - loss: 1.2722
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[36m(train_cnn_ray_tune pid=3064)[0m Epoch 21/27[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3066)[0m 
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[36m(train_cnn_ray_tune pid=3043)[0m 
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[36m(train_cnn_ray_tune pid=3043)[0m 
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[36m(train_cnn_ray_tune pid=3066)[0m 
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[36m(train_cnn_ray_tune pid=3066)[0m 
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[36m(train_cnn_ray_tune pid=3066)[0m 
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[36m(train_cnn_ray_tune pid=3055)[0m 
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[36m(train_cnn_ray_tune pid=3057)[0m 
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[36m(train_cnn_ray_tune pid=3055)[0m 
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[36m(train_cnn_ray_tune pid=3029)[0m 
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[36m(train_cnn_ray_tune pid=3029)[0m 
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[36m(train_cnn_ray_tune pid=3064)[0m Epoch 22/27[32m [repeated 7x across cluster][0m
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[36m(train_cnn_ray_tune pid=3057)[0m 
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[36m(train_cnn_ray_tune pid=3057)[0m 
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[36m(train_cnn_ray_tune pid=3066)[0m 
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Trial status: 13 TERMINATED | 7 RUNNING
Current time: 2025-11-08 10:06:26. Total running time: 5min 30s
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_6f442    RUNNING              3   rmsprop         relu                                   16                 16                  3                 0          0.000196867         23                                              │
│ trial_6f442    RUNNING              3   adam            tanh                                   16                 64                  3                 1          1.25914e-05         23                                              │
│ trial_6f442    RUNNING              2   adam            tanh                                   16                 16                  5                 1          5.51628e-05         28                                              │
│ trial_6f442    RUNNING              3   adam            relu                                   16                 16                  3                 1          1.37357e-05         27                                              │
│ trial_6f442    RUNNING              3   adam            relu                                   16                 32                  3                 1          9.69082e-05         19                                              │
│ trial_6f442    RUNNING              2   adam            tanh                                   16                 16                  5                 0          5.34611e-06         27                                              │
│ trial_6f442    RUNNING              2   rmsprop         tanh                                   16                 16                  5                 0          2.27149e-05         28                                              │
│ trial_6f442    TERMINATED           2   rmsprop         relu                                   32                 64                  3                 0          6.72635e-06         24        1            297.759         0.47507  │
│ trial_6f442    TERMINATED           2   adam            tanh                                   32                 32                  3                 0          0.000114484         16        1            211.277         0.492626 │
│ trial_6f442    TERMINATED           3   adam            tanh                                   32                 64                  5                 1          7.68749e-05         27        1            278.832         0.449087 │
│ trial_6f442    TERMINATED           3   rmsprop         relu                                   32                 32                  5                 1          9.37956e-06         17        1            296.348         0.486657 │
│ trial_6f442    TERMINATED           2   adam            relu                                   32                 32                  3                 1          8.70224e-06         15        1            201.797         0.409761 │
│ trial_6f442    TERMINATED           2   rmsprop         tanh                                   32                 16                  5                 0          2.22726e-05         22        1            260.574         0.351826 │
│ trial_6f442    TERMINATED           2   rmsprop         relu                                   16                 16                  5                 1          0.000135398         18        1            306.842         0.543539 │
│ trial_6f442    TERMINATED           2   adam            relu                                   32                 32                  5                 1          1.00068e-05         24        1            288.182         0.446629 │
│ trial_6f442    TERMINATED           2   rmsprop         tanh                                   16                 64                  5                 1          0.00018307          18        1            247.801         0.452949 │
│ trial_6f442    TERMINATED           3   rmsprop         tanh                                   32                 16                  5                 0          8.77002e-06         18        1            279.442         0.352177 │
│ trial_6f442    TERMINATED           2   rmsprop         relu                                   32                 16                  3                 0          1.65226e-05         24        1            276.9           0.467697 │
│ trial_6f442    TERMINATED           2   adam            tanh                                   32                 64                  3                 0          5.15328e-05         21        1            283.823         0.48736  │
│ trial_6f442    TERMINATED           2   adam            relu                                   32                 64                  3                 1          0.000128258         25        1            233.065         0.552317 │
╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3055)[0m 
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[36m(train_cnn_ray_tune pid=3066)[0m 
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[36m(train_cnn_ray_tune pid=3064)[0m 
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[36m(train_cnn_ray_tune pid=3064)[0m 
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[36m(train_cnn_ray_tune pid=3055)[0m 
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[36m(train_cnn_ray_tune pid=3029)[0m 
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[36m(train_cnn_ray_tune pid=3055)[0m 
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[36m(train_cnn_ray_tune pid=3029)[0m 
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[36m(train_cnn_ray_tune pid=3054)[0m 
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[36m(train_cnn_ray_tune pid=3054)[0m 
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[36m(train_cnn_ray_tune pid=3054)[0m 
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[36m(train_cnn_ray_tune pid=3043)[0m 
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[36m(train_cnn_ray_tune pid=3064)[0m 
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[36m(train_cnn_ray_tune pid=3064)[0m Epoch 23/27[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3043)[0m 
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[36m(train_cnn_ray_tune pid=3054)[0m 
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[36m(train_cnn_ray_tune pid=3054)[0m 
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[36m(train_cnn_ray_tune pid=3055)[0m 
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[36m(train_cnn_ray_tune pid=3029)[0m 
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[36m(train_cnn_ray_tune pid=3066)[0m 
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[36m(train_cnn_ray_tune pid=3064)[0m 
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[36m(train_cnn_ray_tune pid=3055)[0m 
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[36m(train_cnn_ray_tune pid=3043)[0m 
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[36m(train_cnn_ray_tune pid=3029)[0m 
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[36m(train_cnn_ray_tune pid=3064)[0m Epoch 24/27[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3054)[0m 
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[36m(train_cnn_ray_tune pid=3029)[0m 
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[36m(train_cnn_ray_tune pid=3066)[0m 
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[36m(train_cnn_ray_tune pid=3064)[0m 
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[36m(train_cnn_ray_tune pid=3066)[0m 
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[36m(train_cnn_ray_tune pid=3064)[0m 
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[36m(train_cnn_ray_tune pid=3066)[0m 
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[36m(train_cnn_ray_tune pid=3043)[0m 
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[36m(train_cnn_ray_tune pid=3043)[0m 
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[36m(train_cnn_ray_tune pid=3066)[0m 
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[36m(train_cnn_ray_tune pid=3029)[0m 
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[36m(train_cnn_ray_tune pid=3055)[0m 
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[36m(train_cnn_ray_tune pid=3029)[0m 
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[36m(train_cnn_ray_tune pid=3054)[0m 
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[36m(train_cnn_ray_tune pid=3057)[0m Epoch 14/23[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=3057)[0m 
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[36m(train_cnn_ray_tune pid=3064)[0m 
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[36m(train_cnn_ray_tune pid=3064)[0m 
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[36m(train_cnn_ray_tune pid=3066)[0m 
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[36m(train_cnn_ray_tune pid=3057)[0m 
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[36m(train_cnn_ray_tune pid=3066)[0m 
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[36m(train_cnn_ray_tune pid=3066)[0m 
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[36m(train_cnn_ray_tune pid=3043)[0m 
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[36m(train_cnn_ray_tune pid=3064)[0m 
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[36m(train_cnn_ray_tune pid=3029)[0m 
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[36m(train_cnn_ray_tune pid=3029)[0m 
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[36m(train_cnn_ray_tune pid=3055)[0m 
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[36m(train_cnn_ray_tune pid=3054)[0m Epoch 20/23[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3064)[0m 
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[36m(train_cnn_ray_tune pid=3055)[0m 
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[36m(train_cnn_ray_tune pid=3064)[0m 
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[36m(train_cnn_ray_tune pid=3054)[0m 
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[36m(train_cnn_ray_tune pid=3043)[0m 
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[36m(train_cnn_ray_tune pid=3066)[0m 
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[36m(train_cnn_ray_tune pid=3043)[0m 
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[36m(train_cnn_ray_tune pid=3029)[0m 
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[36m(train_cnn_ray_tune pid=3054)[0m 
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[36m(train_cnn_ray_tune pid=3029)[0m Epoch 20/27[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=3066)[0m 
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[36m(train_cnn_ray_tune pid=3066)[0m 
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[36m(train_cnn_ray_tune pid=3064)[0m 
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[36m(train_cnn_ray_tune pid=3029)[0m 
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[36m(train_cnn_ray_tune pid=3066)[0m 
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[36m(train_cnn_ray_tune pid=3029)[0m 
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[36m(train_cnn_ray_tune pid=3029)[0m 
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[36m(train_cnn_ray_tune pid=3043)[0m 
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[36m(train_cnn_ray_tune pid=3043)[0m Epoch 18/19[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=3055)[0m 
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[36m(train_cnn_ray_tune pid=3066)[0m 
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Trial status: 13 TERMINATED | 7 RUNNING
Current time: 2025-11-08 10:06:56. 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_6f442    RUNNING              3   rmsprop         relu                                   16                 16                  3                 0          0.000196867         23                                              │
│ trial_6f442    RUNNING              3   adam            tanh                                   16                 64                  3                 1          1.25914e-05         23                                              │
│ trial_6f442    RUNNING              2   adam            tanh                                   16                 16                  5                 1          5.51628e-05         28                                              │
│ trial_6f442    RUNNING              3   adam            relu                                   16                 16                  3                 1          1.37357e-05         27                                              │
│ trial_6f442    RUNNING              3   adam            relu                                   16                 32                  3                 1          9.69082e-05         19                                              │
│ trial_6f442    RUNNING              2   adam            tanh                                   16                 16                  5                 0          5.34611e-06         27                                              │
│ trial_6f442    RUNNING              2   rmsprop         tanh                                   16                 16                  5                 0          2.27149e-05         28                                              │
│ trial_6f442    TERMINATED           2   rmsprop         relu                                   32                 64                  3                 0          6.72635e-06         24        1            297.759         0.47507  │
│ trial_6f442    TERMINATED           2   adam            tanh                                   32                 32                  3                 0          0.000114484         16        1            211.277         0.492626 │
│ trial_6f442    TERMINATED           3   adam            tanh                                   32                 64                  5                 1          7.68749e-05         27        1            278.832         0.449087 │
│ trial_6f442    TERMINATED           3   rmsprop         relu                                   32                 32                  5                 1          9.37956e-06         17        1            296.348         0.486657 │
│ trial_6f442    TERMINATED           2   adam            relu                                   32                 32                  3                 1          8.70224e-06         15        1            201.797         0.409761 │
│ trial_6f442    TERMINATED           2   rmsprop         tanh                                   32                 16                  5                 0          2.22726e-05         22        1            260.574         0.351826 │
│ trial_6f442    TERMINATED           2   rmsprop         relu                                   16                 16                  5                 1          0.000135398         18        1            306.842         0.543539 │
│ trial_6f442    TERMINATED           2   adam            relu                                   32                 32                  5                 1          1.00068e-05         24        1            288.182         0.446629 │
│ trial_6f442    TERMINATED           2   rmsprop         tanh                                   16                 64                  5                 1          0.00018307          18        1            247.801         0.452949 │
│ trial_6f442    TERMINATED           3   rmsprop         tanh                                   32                 16                  5                 0          8.77002e-06         18        1            279.442         0.352177 │
│ trial_6f442    TERMINATED           2   rmsprop         relu                                   32                 16                  3                 0          1.65226e-05         24        1            276.9           0.467697 │
│ trial_6f442    TERMINATED           2   adam            tanh                                   32                 64                  3                 0          5.15328e-05         21        1            283.823         0.48736  │
│ trial_6f442    TERMINATED           2   adam            relu                                   32                 64                  3                 1          0.000128258         25        1            233.065         0.552317 │
╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3066)[0m 
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[36m(train_cnn_ray_tune pid=3055)[0m 
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[36m(train_cnn_ray_tune pid=3066)[0m 
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[36m(train_cnn_ray_tune pid=3064)[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=3064)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=3055)[0m 
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[36m(train_cnn_ray_tune pid=3064)[0m 
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[36m(train_cnn_ray_tune pid=3064)[0m 
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[36m(train_cnn_ray_tune pid=3029)[0m 
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[36m(train_cnn_ray_tune pid=3064)[0m 
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[36m(train_cnn_ray_tune pid=3064)[0m 
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[36m(train_cnn_ray_tune pid=3029)[0m 
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[36m(train_cnn_ray_tune pid=3064)[0m 
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[36m(train_cnn_ray_tune pid=3064)[0m 
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Trial trial_6f442 finished iteration 1 at 2025-11-08 10:06:58. Total running time: 6min 3s
╭──────────────────────────────────────╮
│ Trial trial_6f442 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             360.199 │
│ time_total_s                 360.199 │
│ training_iteration                 1 │
│ val_accuracy                 0.35218 │
╰──────────────────────────────────────╯

Trial trial_6f442 completed after 1 iterations at 2025-11-08 10:06:58. Total running time: 6min 3s
[36m(train_cnn_ray_tune pid=3066)[0m 
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[36m(train_cnn_ray_tune pid=3054)[0m 
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[36m(train_cnn_ray_tune pid=3043)[0m 
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[36m(train_cnn_ray_tune pid=3057)[0m 
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[36m(train_cnn_ray_tune pid=3057)[0m Epoch 16/23[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3055)[0m 
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[36m(train_cnn_ray_tune pid=3055)[0m 
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[36m(train_cnn_ray_tune pid=3054)[0m 
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[36m(train_cnn_ray_tune pid=3055)[0m 
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Trial trial_6f442 finished iteration 1 at 2025-11-08 10:07:01. Total running time: 6min 6s
[36m(train_cnn_ray_tune pid=3055)[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=3055)[0m   _log_deprecation_warning(
╭──────────────────────────────────────╮
│ Trial trial_6f442 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             363.546 │
│ time_total_s                 363.546 │
│ training_iteration                 1 │
│ val_accuracy                 0.40836 │
╰──────────────────────────────────────╯

Trial trial_6f442 completed after 1 iterations at 2025-11-08 10:07:01. Total running time: 6min 6s
[36m(train_cnn_ray_tune pid=3057)[0m 
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[36m(train_cnn_ray_tune pid=3066)[0m 
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[36m(train_cnn_ray_tune pid=3029)[0m 
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[36m(train_cnn_ray_tune pid=3055)[0m 
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[36m(train_cnn_ray_tune pid=3043)[0m 
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[36m(train_cnn_ray_tune pid=3066)[0m 
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[36m(train_cnn_ray_tune pid=3066)[0m 
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Trial trial_6f442 finished iteration 1 at 2025-11-08 10:07:03. Total running time: 6min 8s
╭──────────────────────────────────────╮
│ Trial trial_6f442 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             365.493 │
│ time_total_s                 365.493 │
│ training_iteration                 1 │
│ val_accuracy                 0.38588 │
╰──────────────────────────────────────╯

Trial trial_6f442 completed after 1 iterations at 2025-11-08 10:07:03. Total running time: 6min 8s
[36m(train_cnn_ray_tune pid=3029)[0m 
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[36m(train_cnn_ray_tune pid=3057)[0m 
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[36m(train_cnn_ray_tune pid=3057)[0m 
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[36m(train_cnn_ray_tune pid=3054)[0m 
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[36m(train_cnn_ray_tune pid=3029)[0m Epoch 22/27[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3054)[0m 
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Trial trial_6f442 finished iteration 1 at 2025-11-08 10:07:06. Total running time: 6min 10s
╭──────────────────────────────────────╮
│ Trial trial_6f442 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             367.888 │
│ time_total_s                 367.888 │
│ training_iteration                 1 │
│ val_accuracy                 0.53968 │
╰──────────────────────────────────────╯

Trial trial_6f442 completed after 1 iterations at 2025-11-08 10:07:06. Total running time: 6min 10s
[36m(train_cnn_ray_tune pid=3054)[0m 
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[36m(train_cnn_ray_tune pid=3057)[0m 
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[36m(train_cnn_ray_tune pid=3054)[0m 
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[36m(train_cnn_ray_tune pid=3043)[0m /home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/ray/train/_internal/session.py:772: RayDeprecationWarning: `ray.train.report` should be switched to `ray.tune.report` when running in a function passed to Ray Tune. This will be an error in the future. See this issue for more context: https://github.com/ray-project/ray/issues/49454[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3043)[0m   _log_deprecation_warning([32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3029)[0m 
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[36m(train_cnn_ray_tune pid=3057)[0m 
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[36m(train_cnn_ray_tune pid=3029)[0m 
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Trial trial_6f442 finished iteration 1 at 2025-11-08 10:07:08. Total running time: 6min 13s
╭──────────────────────────────────────╮
│ Trial trial_6f442 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s              370.66 │
│ time_total_s                  370.66 │
│ training_iteration                 1 │
│ val_accuracy                 0.52598 │
╰──────────────────────────────────────╯

Trial trial_6f442 completed after 1 iterations at 2025-11-08 10:07:08. Total running time: 6min 13s
[36m(train_cnn_ray_tune pid=3029)[0m 
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[36m(train_cnn_ray_tune pid=3057)[0m 
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[36m(train_cnn_ray_tune pid=3029)[0m Epoch 23/27[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3057)[0m 
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[36m(train_cnn_ray_tune pid=3057)[0m 
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[36m(train_cnn_ray_tune pid=3043)[0m 
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[36m(train_cnn_ray_tune pid=3057)[0m 
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[36m(train_cnn_ray_tune pid=3029)[0m 
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[36m(train_cnn_ray_tune pid=3029)[0m Epoch 24/27[32m [repeated 2x across cluster][0m
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[36m(train_cnn_ray_tune pid=3029)[0m 
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[36m(train_cnn_ray_tune pid=3057)[0m 
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[36m(train_cnn_ray_tune pid=3057)[0m 
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[36m(train_cnn_ray_tune pid=3029)[0m 
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[36m(train_cnn_ray_tune pid=3057)[0m 
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[36m(train_cnn_ray_tune pid=3029)[0m 
[1m  1/655[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m23s[0m 35ms/step - accuracy: 0.3125 - loss: 1.1968
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[36m(train_cnn_ray_tune pid=3029)[0m Epoch 26/27[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3057)[0m 
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[36m(train_cnn_ray_tune pid=3057)[0m 
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[36m(train_cnn_ray_tune pid=3029)[0m 
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[36m(train_cnn_ray_tune pid=3057)[0m 
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[36m(train_cnn_ray_tune pid=3029)[0m 
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[36m(train_cnn_ray_tune pid=3029)[0m Epoch 27/27[32m [repeated 2x across cluster][0m

Trial status: 18 TERMINATED | 2 RUNNING
Current time: 2025-11-08 10:07:26. Total running time: 6min 31s
Logical resource usage: 2.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     num_resblocks     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_6f442    RUNNING              3   adam            tanh                                   16                 64                  3                 1          1.25914e-05         23                                              │
│ trial_6f442    RUNNING              3   adam            relu                                   16                 16                  3                 1          1.37357e-05         27                                              │
│ trial_6f442    TERMINATED           2   rmsprop         relu                                   32                 64                  3                 0          6.72635e-06         24        1            297.759         0.47507  │
│ trial_6f442    TERMINATED           2   adam            tanh                                   32                 32                  3                 0          0.000114484         16        1            211.277         0.492626 │
│ trial_6f442    TERMINATED           3   adam            tanh                                   32                 64                  5                 1          7.68749e-05         27        1            278.832         0.449087 │
│ trial_6f442    TERMINATED           3   rmsprop         relu                                   32                 32                  5                 1          9.37956e-06         17        1            296.348         0.486657 │
│ trial_6f442    TERMINATED           2   adam            relu                                   32                 32                  3                 1          8.70224e-06         15        1            201.797         0.409761 │
│ trial_6f442    TERMINATED           2   rmsprop         tanh                                   32                 16                  5                 0          2.22726e-05         22        1            260.574         0.351826 │
│ trial_6f442    TERMINATED           3   rmsprop         relu                                   16                 16                  3                 0          0.000196867         23        1            367.888         0.539677 │
│ trial_6f442    TERMINATED           2   adam            tanh                                   16                 16                  5                 1          5.51628e-05         28        1            363.547         0.408357 │
│ trial_6f442    TERMINATED           2   rmsprop         relu                                   16                 16                  5                 1          0.000135398         18        1            306.842         0.543539 │
│ trial_6f442    TERMINATED           2   adam            relu                                   32                 32                  5                 1          1.00068e-05         24        1            288.182         0.446629 │
│ trial_6f442    TERMINATED           2   rmsprop         tanh                                   16                 64                  5                 1          0.00018307          18        1            247.801         0.452949 │
│ trial_6f442    TERMINATED           3   adam            relu                                   16                 32                  3                 1          9.69082e-05         19        1            370.66          0.525983 │
│ trial_6f442    TERMINATED           3   rmsprop         tanh                                   32                 16                  5                 0          8.77002e-06         18        1            279.442         0.352177 │
│ trial_6f442    TERMINATED           2   adam            tanh                                   16                 16                  5                 0          5.34611e-06         27        1            360.199         0.352177 │
│ trial_6f442    TERMINATED           2   rmsprop         tanh                                   16                 16                  5                 0          2.27149e-05         28        1            365.493         0.385885 │
│ trial_6f442    TERMINATED           2   rmsprop         relu                                   32                 16                  3                 0          1.65226e-05         24        1            276.9           0.467697 │
│ trial_6f442    TERMINATED           2   adam            tanh                                   32                 64                  3                 0          5.15328e-05         21        1            283.823         0.48736  │
│ trial_6f442    TERMINATED           2   adam            relu                                   32                 64                  3                 1          0.000128258         25        1            233.065         0.552317 │
╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
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[36m(train_cnn_ray_tune pid=3029)[0m 
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[36m(train_cnn_ray_tune pid=3029)[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=3029)[0m   _log_deprecation_warning(
2025-11-08 10:07:35,795	INFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/mnt/nvme1n2/git/uniovi-simur-wearablepermed-data/output/Paper_results/cases_dataset_M/case_M_CAPTURE24_acc_gyr_superclasses_CPA_METs/CAPTURE24_hyperparameters_tuning' in 0.0067s.
[36m(train_cnn_ray_tune pid=3029)[0m 
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[36m(train_cnn_ray_tune pid=3029)[0m 
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Trial trial_6f442 finished iteration 1 at 2025-11-08 10:07:29. Total running time: 6min 34s
╭──────────────────────────────────────╮
│ Trial trial_6f442 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s              390.98 │
│ time_total_s                  390.98 │
│ training_iteration                 1 │
│ val_accuracy                 0.42942 │
╰──────────────────────────────────────╯

Trial trial_6f442 completed after 1 iterations at 2025-11-08 10:07:29. Total running time: 6min 34s
[36m(train_cnn_ray_tune pid=3029)[0m 
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[36m(train_cnn_ray_tune pid=3057)[0m 
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Trial trial_6f442 finished iteration 1 at 2025-11-08 10:07:35. Total running time: 6min 40s
╭──────────────────────────────────────╮
│ Trial trial_6f442 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             397.497 │
│ time_total_s                 397.497 │
│ training_iteration                 1 │
│ val_accuracy                 0.48666 │
╰──────────────────────────────────────╯

Trial trial_6f442 completed after 1 iterations at 2025-11-08 10:07:35. Total running time: 6min 40s

Trial status: 20 TERMINATED
Current time: 2025-11-08 10:07:35. Total running time: 6min 40s
Logical resource usage: 1.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
I0000 00:00:1762592855.922838    1242 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=3057)[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=3057)[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_6f442    TERMINATED           2   rmsprop         relu                                   32                 64                  3                 0          6.72635e-06         24        1            297.759         0.47507  │
│ trial_6f442    TERMINATED           2   adam            tanh                                   32                 32                  3                 0          0.000114484         16        1            211.277         0.492626 │
│ trial_6f442    TERMINATED           3   adam            tanh                                   32                 64                  5                 1          7.68749e-05         27        1            278.832         0.449087 │
│ trial_6f442    TERMINATED           3   rmsprop         relu                                   32                 32                  5                 1          9.37956e-06         17        1            296.348         0.486657 │
│ trial_6f442    TERMINATED           2   adam            relu                                   32                 32                  3                 1          8.70224e-06         15        1            201.797         0.409761 │
│ trial_6f442    TERMINATED           2   rmsprop         tanh                                   32                 16                  5                 0          2.22726e-05         22        1            260.574         0.351826 │
│ trial_6f442    TERMINATED           3   rmsprop         relu                                   16                 16                  3                 0          0.000196867         23        1            367.888         0.539677 │
│ trial_6f442    TERMINATED           3   adam            tanh                                   16                 64                  3                 1          1.25914e-05         23        1            397.497         0.486657 │
│ trial_6f442    TERMINATED           2   adam            tanh                                   16                 16                  5                 1          5.51628e-05         28        1            363.547         0.408357 │
│ trial_6f442    TERMINATED           2   rmsprop         relu                                   16                 16                  5                 1          0.000135398         18        1            306.842         0.543539 │
│ trial_6f442    TERMINATED           3   adam            relu                                   16                 16                  3                 1          1.37357e-05         27        1            390.98          0.429424 │
│ trial_6f442    TERMINATED           2   adam            relu                                   32                 32                  5                 1          1.00068e-05         24        1            288.182         0.446629 │
│ trial_6f442    TERMINATED           2   rmsprop         tanh                                   16                 64                  5                 1          0.00018307          18        1            247.801         0.452949 │
│ trial_6f442    TERMINATED           3   adam            relu                                   16                 32                  3                 1          9.69082e-05         19        1            370.66          0.525983 │
│ trial_6f442    TERMINATED           3   rmsprop         tanh                                   32                 16                  5                 0          8.77002e-06         18        1            279.442         0.352177 │
│ trial_6f442    TERMINATED           2   adam            tanh                                   16                 16                  5                 0          5.34611e-06         27        1            360.199         0.352177 │
│ trial_6f442    TERMINATED           2   rmsprop         tanh                                   16                 16                  5                 0          2.27149e-05         28        1            365.493         0.385885 │
│ trial_6f442    TERMINATED           2   rmsprop         relu                                   32                 16                  3                 0          1.65226e-05         24        1            276.9           0.467697 │
│ trial_6f442    TERMINATED           2   adam            tanh                                   32                 64                  3                 0          5.15328e-05         21        1            283.823         0.48736  │
│ trial_6f442    TERMINATED           2   adam            relu                                   32                 64                  3                 1          0.000128258         25        1            233.065         0.552317 │
╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

Mejores hiperparámetros: {'N_capas': 2, 'optimizador': 'adam', 'funcion_activacion': 'relu', 'tamanho_minilote': 32, 'numero_filtros': 64, 'tamanho_filtro': 3, 'num_resblocks': 1, 'tasa_aprendizaje': 0.00012825806388861303, 'epochs': 25}
[36m(train_cnn_ray_tune pid=3057)[0m 
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Epoch 1/25
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762592858.474147   48330 service.cc:152] XLA service 0x7a5abc0018e0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762592858.474178   48330 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-08 10:07:38.524695: 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 00:00:1762592858.851032   48330 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762592861.036172   48330 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 2/25

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Epoch 3/25

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Epoch 4/25

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Epoch 5/25

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[1m273/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4931 - loss: 1.1882
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Epoch 6/25

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[1m113/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5136 - loss: 1.1431
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[1m229/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5183 - loss: 1.1399
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[1m288/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5190 - loss: 1.1397
[1m318/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.5191 - loss: 1.1397
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5190 - loss: 1.1398 - val_accuracy: 0.5323 - val_loss: 1.1223
Epoch 7/25

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[1m 31/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5236 - loss: 1.1201 
[1m 61/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5356 - loss: 1.1057
[1m 92/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5368 - loss: 1.1031
[1m121/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5364 - loss: 1.1039
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[1m179/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5356 - loss: 1.1047
[1m210/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5349 - loss: 1.1054
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[1m269/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5334 - loss: 1.1070
[1m300/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5330 - loss: 1.1074
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Epoch 8/25

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5000 - loss: 1.1590
[1m 31/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5222 - loss: 1.0828 
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Epoch 9/25

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5625 - loss: 1.1378
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Epoch 10/25

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[1m209/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5986 - loss: 0.9791
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[1m271/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5997 - loss: 0.9762
[1m302/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.6003 - loss: 0.9753
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[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.6006 - loss: 0.9748 - val_accuracy: 0.5607 - val_loss: 1.0915
Epoch 11/25

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[1m 28/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6240 - loss: 0.9082 
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[1m118/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6268 - loss: 0.9199
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Epoch 12/25

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[1m291/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6355 - loss: 0.9081
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Epoch 13/25

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[1m112/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6545 - loss: 0.8629
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[1m293/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6583 - loss: 0.8567
[1m323/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.6582 - loss: 0.8566
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.6582 - loss: 0.8566 - val_accuracy: 0.5632 - val_loss: 1.1304
Epoch 14/25

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[1m 32/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6912 - loss: 0.7860 
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[1m 91/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6823 - loss: 0.8060
[1m121/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6813 - loss: 0.8089
[1m150/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6798 - loss: 0.8122
[1m182/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6792 - loss: 0.8147
[1m213/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6790 - loss: 0.8161
[1m240/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6786 - loss: 0.8172
[1m271/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6782 - loss: 0.8181
[1m302/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.6781 - loss: 0.8184
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.6780 - loss: 0.8185 - val_accuracy: 0.5530 - val_loss: 1.1844
Epoch 15/25

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.6250 - loss: 0.8465
[1m 30/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6766 - loss: 0.8056 
[1m 59/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6841 - loss: 0.7928
[1m 89/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6862 - loss: 0.7889
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[1m150/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6865 - loss: 0.7892
[1m181/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6876 - loss: 0.7882
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[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6895 - loss: 0.7862
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Saved model to disk.
[36m(train_cnn_ray_tune pid=3057)[0m 
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[36m(train_cnn_ray_tune pid=3057)[0m 
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2025-11-08 10:08:09.564903: 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 10:08:09.576270: 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 00:00:1762592889.589359   50638 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762592889.593446   50638 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762592889.603262   50638 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762592889.603278   50638 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762592889.603280   50638 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762592889.603281   50638 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-08 10:08:09.606440: 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 00:00:1762592891.967809   50638 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 1/25
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762592894.511851   50754 service.cc:152] XLA service 0x791a300036b0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762592894.511885   50754 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-08 10:08:14.561811: 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 00:00:1762592894.883993   50754 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762592897.027927   50754 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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[1m212/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3382 - loss: 1.6627
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[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m8s[0m 12ms/step - accuracy: 0.3472 - loss: 1.6259 - val_accuracy: 0.4638 - val_loss: 1.3633
Epoch 2/25

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[1m 96/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3900 - loss: 1.3991
[1m124/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3924 - loss: 1.3959
[1m153/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3943 - loss: 1.3932
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Epoch 3/25

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Epoch 4/25

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[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4515 - loss: 1.2442
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Epoch 5/25

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[1m170/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4818 - loss: 1.2020
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[1m232/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4828 - loss: 1.2011
[1m259/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4833 - loss: 1.2004
[1m290/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4834 - loss: 1.1997
[1m318/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.4835 - loss: 1.1992
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Epoch 6/25

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[1m121/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4881 - loss: 1.1714
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Epoch 7/25

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Epoch 8/25

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Epoch 9/25

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Epoch 10/25

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Epoch 11/25

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[1m269/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5946 - loss: 0.9787
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Epoch 12/25

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Epoch 13/25

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[1m 92/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6444 - loss: 0.8765
[1m122/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6451 - loss: 0.8763
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[1m275/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6463 - loss: 0.8763
[1m305/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.6462 - loss: 0.8766
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.6461 - loss: 0.8768 - val_accuracy: 0.5586 - val_loss: 1.1450
Epoch 14/25

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 24ms/step - accuracy: 0.7812 - loss: 0.5555
[1m 27/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6703 - loss: 0.7947 
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Epoch 15/25

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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m20s[0m 420ms/step2025-11-08 10:08:32.251193: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_141', 8 bytes spill stores, 8 bytes spill loads

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[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 21ms/step  
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 21ms/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}
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Loaded model from disk.
(1545, 6, 250)
(10469, 6, 250)

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6:45[0m 1s/step
[1m 60/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 856us/step
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[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 17ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 18ms/step

[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m49/89[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step 
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step
Global accuracy score (validation) = 54.6 [%]
Global F1 score (validation) = 54.02 [%]
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[[0.2433151  0.12561014 0.4464236  0.18465118]
 [0.22412516 0.12856032 0.5401879  0.10712661]
 [0.21351065 0.12802622 0.5755349  0.08292825]
 ...
 [0.03492177 0.02691639 0.0325257  0.9056361 ]
 [0.0402686  0.03365487 0.04622835 0.8798482 ]
 [0.40447238 0.23342064 0.14767683 0.21443018]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 69.56 [%]
Global accuracy score (test) = 51.13 [%]
Global F1 score (train) = 69.22 [%]
Global F1 score (test) = 51.28 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.37      0.32      0.34       400
MODERATE-INTENSITY       0.44      0.49      0.46       400
         SEDENTARY       0.71      0.61      0.66       400
VIGOROUS-INTENSITY       0.54      0.65      0.59       345

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


Accuracy capturado en la ejecución 1: 51.13 [%]
F1-score capturado en la ejecución 1: 51.28 [%]

=== EJECUCIÓN 2 ===

--- TRAIN (ejecución 2) ---

--- TEST (ejecución 2) ---
2025-11-08 10:08:43.314349: 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 10:08:43.325563: 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 00:00:1762592923.338590   53055 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762592923.342695   53055 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762592923.352383   53055 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762592923.352400   53055 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762592923.352402   53055 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762592923.352403   53055 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-08 10:08:43.355528: 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 00:00:1762592925.676262   53055 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 1/25
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762592928.233560   53165 service.cc:152] XLA service 0x7d85a4006410 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762592928.233594   53165 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-08 10:08:48.283658: 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 00:00:1762592928.605653   53165 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762592930.784453   53165 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 2/25

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Epoch 3/25

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Epoch 4/25

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Epoch 5/25

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Epoch 6/25

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[1m273/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5011 - loss: 1.1680
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Epoch 7/25

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[1m 28/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5551 - loss: 1.0992 
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[1m120/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5595 - loss: 1.0911
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[1m181/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5547 - loss: 1.0948
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Epoch 8/25

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[1m 28/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5621 - loss: 1.0459 
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[1m238/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5506 - loss: 1.0755
[1m267/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5504 - loss: 1.0754
[1m300/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5501 - loss: 1.0755
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5500 - loss: 1.0754 - val_accuracy: 0.5435 - val_loss: 1.1100
Epoch 9/25

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[1m 33/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5714 - loss: 0.9933 
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[1m274/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5798 - loss: 1.0090
[1m304/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5793 - loss: 1.0103
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Epoch 10/25

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[1m119/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5652 - loss: 1.0240
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[1m175/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5712 - loss: 1.0180
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[1m235/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5743 - loss: 1.0145
[1m267/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5755 - loss: 1.0126
[1m297/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5766 - loss: 1.0109
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5778 - loss: 1.0091 - val_accuracy: 0.5435 - val_loss: 1.1260
Epoch 11/25

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[1m 30/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5776 - loss: 1.0149 
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[1m 90/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5913 - loss: 0.9847
[1m120/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5954 - loss: 0.9807
[1m150/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5982 - loss: 0.9781
[1m183/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6013 - loss: 0.9744
[1m213/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6034 - loss: 0.9716
[1m245/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6049 - loss: 0.9695
[1m275/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6061 - loss: 0.9678
[1m306/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.6068 - loss: 0.9662
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.6072 - loss: 0.9653 - val_accuracy: 0.5593 - val_loss: 1.1161
Epoch 12/25

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5625 - loss: 0.9444
[1m 29/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6239 - loss: 0.9242 
[1m 59/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6306 - loss: 0.9192
[1m 87/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6345 - loss: 0.9121
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Epoch 13/25

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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m19s[0m 414ms/step2025-11-08 10:09:04.612855: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_141', 8 bytes spill stores, 8 bytes spill loads

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Saved model to disk.
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Loaded model from disk.
(1545, 6, 250)
(10469, 6, 250)

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6:55[0m 1s/step
[1m 50/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step 
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[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 18ms/step

[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m62/89[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 827us/step
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 974us/step
Global accuracy score (validation) = 54.78 [%]
Global F1 score (validation) = 53.36 [%]
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[[0.16995116 0.17924453 0.6058735  0.04493076]
 [0.16834876 0.17434818 0.61002547 0.04727763]
 [0.16263248 0.17030154 0.631183   0.03588287]
 ...
 [0.08849509 0.05057864 0.06120558 0.7997207 ]
 [0.19300413 0.11346614 0.1394091  0.5541207 ]
 [0.05623236 0.03629958 0.03484917 0.87261885]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 68.88 [%]
Global accuracy score (test) = 52.94 [%]
Global F1 score (train) = 68.69 [%]
Global F1 score (test) = 52.32 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.43      0.30      0.35       400
MODERATE-INTENSITY       0.46      0.53      0.49       400
         SEDENTARY       0.64      0.70      0.67       400
VIGOROUS-INTENSITY       0.57      0.60      0.58       345

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


Accuracy capturado en la ejecución 2: 52.94 [%]
F1-score capturado en la ejecución 2: 52.32 [%]

=== EJECUCIÓN 3 ===

--- TRAIN (ejecución 3) ---

--- TEST (ejecución 3) ---
2025-11-08 10:09:15.508250: 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 10:09:15.519240: 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 00:00:1762592955.532157   55265 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762592955.536239   55265 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762592955.546155   55265 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762592955.546171   55265 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762592955.546172   55265 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762592955.546173   55265 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-08 10:09:15.549091: 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 00:00:1762592957.886837   55265 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)
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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)
Epoch 1/25
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762592960.355509   55407 service.cc:152] XLA service 0x72a848005ab0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762592960.355536   55407 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-08 10:09:20.404500: 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 00:00:1762592960.713147   55407 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762592962.882472   55407 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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[1m235/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3150 - loss: 1.7004
[1m262/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3188 - loss: 1.6882
[1m289/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3220 - loss: 1.6771
[1m317/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.3251 - loss: 1.6665
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step - accuracy: 0.3262 - loss: 1.6625
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m8s[0m 12ms/step - accuracy: 0.3263 - loss: 1.6622 - val_accuracy: 0.4540 - val_loss: 1.2834
Epoch 2/25

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[1m 27/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4106 - loss: 1.4243 
[1m 55/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4122 - loss: 1.3985
[1m 84/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4095 - loss: 1.3925
[1m114/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4080 - loss: 1.3892
[1m143/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4067 - loss: 1.3873
[1m166/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4057 - loss: 1.3863
[1m193/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4049 - loss: 1.3853
[1m219/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4044 - loss: 1.3840
[1m248/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4040 - loss: 1.3830
[1m275/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4038 - loss: 1.3821
[1m304/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4037 - loss: 1.3813
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4039 - loss: 1.3805 - val_accuracy: 0.4691 - val_loss: 1.2198
Epoch 3/25

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 23ms/step - accuracy: 0.3750 - loss: 1.2988
[1m 27/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4198 - loss: 1.3031 
[1m 57/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4276 - loss: 1.3024
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[1m118/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4338 - loss: 1.2974
[1m143/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4341 - loss: 1.2963
[1m174/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4337 - loss: 1.2962
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[1m267/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4328 - loss: 1.2958
[1m298/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4327 - loss: 1.2955
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Epoch 4/25

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Epoch 5/25

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Epoch 6/25

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[1m292/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5048 - loss: 1.1558
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[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5056 - loss: 1.1552 - val_accuracy: 0.5274 - val_loss: 1.1260
Epoch 7/25

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[1m 30/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5457 - loss: 1.1007 
[1m 59/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5382 - loss: 1.1110
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[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5296 - loss: 1.1165
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Epoch 8/25

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[1m 31/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5305 - loss: 1.1007 
[1m 62/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5425 - loss: 1.0894
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[1m248/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5582 - loss: 1.0685
[1m279/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5582 - loss: 1.0679
[1m308/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5580 - loss: 1.0675
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Epoch 9/25

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[1m 30/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5908 - loss: 1.0256 
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[1m 88/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5825 - loss: 1.0291
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[1m229/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5747 - loss: 1.0353
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[1m286/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5748 - loss: 1.0342
[1m316/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.5749 - loss: 1.0333
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Epoch 10/25

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[1m182/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6008 - loss: 0.9946
[1m212/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6004 - loss: 0.9952
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[1m274/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5999 - loss: 0.9947
[1m305/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5998 - loss: 0.9941
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Epoch 11/25

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[1m 25/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6194 - loss: 0.9562 
[1m 55/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6253 - loss: 0.9488
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[1m114/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6263 - loss: 0.9462
[1m145/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6266 - loss: 0.9460
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Epoch 12/25

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Epoch 13/25

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Epoch 14/25

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[1m290/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6800 - loss: 0.8141
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Epoch 15/25

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Saved model to disk.
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Loaded model from disk.
(1545, 6, 250)
(10469, 6, 250)

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6:44[0m 1s/step
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[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m46/89[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step 
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Global accuracy score (validation) = 54.92 [%]
Global F1 score (validation) = 53.29 [%]
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[[0.21122989 0.24918501 0.23580375 0.3037814 ]
 [0.3115717  0.28342324 0.23964317 0.16536191]
 [0.20126987 0.08408559 0.6795161  0.03512854]
 ...
 [0.25020736 0.07371414 0.28610465 0.38997385]
 [0.17348665 0.04652167 0.30470818 0.47528353]
 [0.08135751 0.02204918 0.07633077 0.8202626 ]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 66.17 [%]
Global accuracy score (test) = 53.92 [%]
Global F1 score (train) = 66.25 [%]
Global F1 score (test) = 53.04 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.45      0.32      0.37       400
MODERATE-INTENSITY       0.50      0.52      0.51       400
         SEDENTARY       0.57      0.78      0.65       400
VIGOROUS-INTENSITY       0.64      0.54      0.59       345

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


Accuracy capturado en la ejecución 3: 53.92 [%]
F1-score capturado en la ejecución 3: 53.04 [%]

=== EJECUCIÓN 4 ===

--- TRAIN (ejecución 4) ---

--- TEST (ejecución 4) ---
2025-11-08 10:09:49.401192: 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 10:09:49.412644: 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 00:00:1762592989.426042   57702 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762592989.430232   57702 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762592989.440077   57702 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762592989.440094   57702 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762592989.440095   57702 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762592989.440096   57702 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-08 10:09:49.443350: 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 00:00:1762592991.813578   57702 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 1/25
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762592994.319422   57832 service.cc:152] XLA service 0x75f0b8006b70 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762592994.319481   57832 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-08 10:09:54.372672: 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 00:00:1762592994.700252   57832 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762592996.840608   57832 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m23:45[0m 4s/step - accuracy: 0.2812 - loss: 1.9022
[1m 25/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3194 - loss: 1.7437  
[1m 54/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3352 - loss: 1.7103
[1m 82/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3417 - loss: 1.6914
[1m112/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3442 - loss: 1.6824
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[1m171/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3457 - loss: 1.6689
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Epoch 2/25

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[1m295/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4049 - loss: 1.4207
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Epoch 3/25

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[1m115/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4187 - loss: 1.3108
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[1m266/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4244 - loss: 1.3058
[1m297/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4253 - loss: 1.3046
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4257 - loss: 1.3037
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4257 - loss: 1.3037 - val_accuracy: 0.5039 - val_loss: 1.1451
Epoch 4/25

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[1m 28/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4587 - loss: 1.2443 
[1m 59/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4538 - loss: 1.2455
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[1m123/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4548 - loss: 1.2489
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[1m187/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4551 - loss: 1.2503
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Epoch 5/25

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Epoch 6/25

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Epoch 7/25

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[1m269/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5288 - loss: 1.1161
[1m298/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5290 - loss: 1.1149
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Epoch 8/25

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[1m 26/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5713 - loss: 1.0349 
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[1m112/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5541 - loss: 1.0614
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Epoch 9/25

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Epoch 10/25

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[1m298/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5878 - loss: 0.9887
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Epoch 11/25

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[1m123/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5983 - loss: 0.9522
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[1m290/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6020 - loss: 0.9507
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[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.6022 - loss: 0.9518 - val_accuracy: 0.5632 - val_loss: 1.0869
Epoch 12/25

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[1m118/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6337 - loss: 0.8998
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[1m298/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.6283 - loss: 0.9113
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Epoch 13/25

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.7188 - loss: 0.7566
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Epoch 14/25

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Epoch 15/25

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[1m 89/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6635 - loss: 0.8484
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[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.6673 - loss: 0.8351 - val_accuracy: 0.5632 - val_loss: 1.1293

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m20s[0m 430ms/step2025-11-08 10:10:12.238477: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_141', 8 bytes spill stores, 8 bytes spill loads


[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 21ms/step  
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 21ms/step
Saved model to disk.
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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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)
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This activity can't be balanced (in a downsampling way)
Loaded model from disk.
(1545, 6, 250)
(10469, 6, 250)

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[1m174/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 871us/step
[1m237/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 852us/step
[1m293/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 862us/step
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step  
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m3s[0m 4ms/step

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 23ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 17ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 18ms/step

[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 17ms/step
[1m60/89[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 860us/step
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step  
Global accuracy score (validation) = 55.51 [%]
Global F1 score (validation) = 54.29 [%]
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[[0.13111739 0.08727527 0.71209425 0.06951308]
 [0.09978504 0.10913239 0.772346   0.01873646]
 [0.14238875 0.11436239 0.6913797  0.05186921]
 ...
 [0.15941231 0.07478043 0.31080016 0.45500714]
 [0.19437967 0.14793843 0.18995321 0.4677286 ]
 [0.3021466  0.25777924 0.33075523 0.10931896]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 71.29 [%]
Global accuracy score (test) = 55.66 [%]
Global F1 score (train) = 71.4 [%]
Global F1 score (test) = 55.36 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.45      0.35      0.39       400
MODERATE-INTENSITY       0.50      0.61      0.55       400
         SEDENTARY       0.58      0.72      0.64       400
VIGOROUS-INTENSITY       0.73      0.55      0.62       345

          accuracy                           0.56      1545
         macro avg       0.57      0.56      0.55      1545
      weighted avg       0.56      0.56      0.55      1545


Accuracy capturado en la ejecución 4: 55.66 [%]
F1-score capturado en la ejecución 4: 55.36 [%]

=== EJECUCIÓN 5 ===

--- TRAIN (ejecución 5) ---

--- TEST (ejecución 5) ---
2025-11-08 10:10:23.272225: 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 10:10:23.283942: 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 00:00:1762593023.297293   60130 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762593023.301603   60130 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762593023.312128   60130 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762593023.312145   60130 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762593023.312147   60130 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762593023.312148   60130 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-08 10:10:23.315480: 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 00:00:1762593025.650088   60130 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 1/25
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762593028.203950   60240 service.cc:152] XLA service 0x767e88006690 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762593028.203992   60240 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-08 10:10:28.265761: 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 00:00:1762593028.591930   60240 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762593030.749635   60240 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m24:05[0m 4s/step - accuracy: 0.2812 - loss: 1.7874
[1m 24/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2822 - loss: 1.7579  
[1m 55/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3020 - loss: 1.7219
[1m 84/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3113 - loss: 1.7018
[1m113/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3181 - loss: 1.6836
[1m144/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3236 - loss: 1.6659
[1m174/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3288 - loss: 1.6507
[1m203/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3326 - loss: 1.6389
[1m229/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3353 - loss: 1.6303
[1m257/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3380 - loss: 1.6216
[1m287/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3405 - loss: 1.6132
[1m316/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.3427 - loss: 1.6056
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step - accuracy: 0.3435 - loss: 1.6027
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m8s[0m 12ms/step - accuracy: 0.3436 - loss: 1.6024 - val_accuracy: 0.4579 - val_loss: 1.2393
Epoch 2/25

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 24ms/step - accuracy: 0.4062 - loss: 1.2304
[1m 29/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3994 - loss: 1.3615 
[1m 59/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4011 - loss: 1.3722
[1m 88/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4009 - loss: 1.3803
[1m119/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4000 - loss: 1.3826
[1m147/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4004 - loss: 1.3819
[1m177/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4007 - loss: 1.3812
[1m208/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4011 - loss: 1.3815
[1m240/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4016 - loss: 1.3812
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Epoch 3/25

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Epoch 4/25

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Epoch 5/25

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[1m 28/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4711 - loss: 1.1840 
[1m 58/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4641 - loss: 1.1905
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[1m119/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4652 - loss: 1.2000
[1m148/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4671 - loss: 1.2012
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[1m209/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4703 - loss: 1.2009
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[1m292/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4726 - loss: 1.2013
[1m323/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.4733 - loss: 1.2014
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4734 - loss: 1.2014 - val_accuracy: 0.4982 - val_loss: 1.1441
Epoch 6/25

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[1m 31/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5218 - loss: 1.1594 
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Epoch 7/25

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[1m265/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5262 - loss: 1.1286
[1m293/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5258 - loss: 1.1281
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Epoch 8/25

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[1m265/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5483 - loss: 1.0794
[1m296/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5480 - loss: 1.0798
[1m327/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.5480 - loss: 1.0800
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5480 - loss: 1.0800 - val_accuracy: 0.5323 - val_loss: 1.0965
Epoch 9/25

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[1m 29/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5816 - loss: 1.0058 
[1m 59/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5781 - loss: 1.0068
[1m 89/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5728 - loss: 1.0181
[1m117/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5692 - loss: 1.0259
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[1m173/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5691 - loss: 1.0298
[1m203/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5698 - loss: 1.0304
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[1m289/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5703 - loss: 1.0324
[1m316/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.5702 - loss: 1.0328
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5703 - loss: 1.0328 - val_accuracy: 0.5302 - val_loss: 1.0988
Epoch 10/25

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Epoch 11/25

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[1m294/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6090 - loss: 0.9580
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Epoch 12/25

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[1m117/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6277 - loss: 0.9151
[1m149/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6270 - loss: 0.9152
[1m180/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6266 - loss: 0.9153
[1m211/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6263 - loss: 0.9155
[1m242/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6259 - loss: 0.9159
[1m271/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6255 - loss: 0.9163
[1m302/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.6251 - loss: 0.9168
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.6250 - loss: 0.9170 - val_accuracy: 0.5253 - val_loss: 1.1152
Epoch 13/25

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.6562 - loss: 0.7986
[1m 29/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6338 - loss: 0.8690 
[1m 59/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6429 - loss: 0.8698
[1m 88/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6458 - loss: 0.8705
[1m118/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6452 - loss: 0.8726
[1m149/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6438 - loss: 0.8755
[1m180/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6424 - loss: 0.8781
[1m209/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6418 - loss: 0.8794
[1m235/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6415 - loss: 0.8799
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[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.6412 - loss: 0.8805 - val_accuracy: 0.5376 - val_loss: 1.1245

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m19s[0m 413ms/step2025-11-08 10:10:44.629231: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_141', 8 bytes spill stores, 8 bytes spill loads

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Saved model to disk.
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Loaded model from disk.
(1545, 6, 250)
(10469, 6, 250)

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6:52[0m 1s/step
[1m 51/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step 
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[1m170/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 892us/step
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[1m289/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 874us/step
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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 16ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 18ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 18ms/step

[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 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) = 55.79 [%]
Global F1 score (validation) = 54.96 [%]
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[[0.16304362 0.08058279 0.71225274 0.04412082]
 [0.13199    0.07593787 0.7721269  0.01994516]
 [0.13628846 0.07630347 0.76273334 0.02467474]
 ...
 [0.24267735 0.11018153 0.4322529  0.2148882 ]
 [0.2834423  0.0556313  0.2055788  0.45534757]
 [0.23989698 0.08507448 0.07759397 0.5974346 ]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 69.6 [%]
Global accuracy score (test) = 52.82 [%]
Global F1 score (train) = 69.81 [%]
Global F1 score (test) = 52.64 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.39      0.34      0.36       400
MODERATE-INTENSITY       0.49      0.56      0.52       400
         SEDENTARY       0.60      0.69      0.64       400
VIGOROUS-INTENSITY       0.65      0.53      0.58       345

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


Accuracy capturado en la ejecución 5: 52.82 [%]
F1-score capturado en la ejecución 5: 52.64 [%]

=== EJECUCIÓN 6 ===

--- TRAIN (ejecución 6) ---

--- TEST (ejecución 6) ---
2025-11-08 10:10:55.550089: 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 10:10:55.562103: 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 00:00:1762593055.575431   62342 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762593055.579648   62342 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762593055.589558   62342 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762593055.589580   62342 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762593055.589581   62342 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762593055.589582   62342 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-08 10:10:55.592794: 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 00:00:1762593057.931931   62342 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 1/25
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762593060.404800   62475 service.cc:152] XLA service 0x73bb58017a20 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762593060.404832   62475 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-08 10:11:00.457451: 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 00:00:1762593060.781878   62475 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762593062.906026   62475 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m23:30[0m 4s/step - accuracy: 0.1562 - loss: 2.1725
[1m 22/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2615 - loss: 1.8229  
[1m 51/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2784 - loss: 1.7820
[1m 82/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2912 - loss: 1.7518
[1m115/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3013 - loss: 1.7269
[1m142/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3072 - loss: 1.7110
[1m173/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3126 - loss: 1.6951
[1m202/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3171 - loss: 1.6816
[1m232/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3210 - loss: 1.6690
[1m261/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3244 - loss: 1.6577
[1m292/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3277 - loss: 1.6467
[1m319/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.3304 - loss: 1.6379
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step - accuracy: 0.3313 - loss: 1.6350
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Epoch 2/25

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Epoch 3/25

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Epoch 4/25

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Epoch 5/25

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Epoch 6/25

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Epoch 7/25

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Epoch 8/25

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[1m 29/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5336 - loss: 1.1287 
[1m 57/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5400 - loss: 1.1139
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[1m114/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5451 - loss: 1.1002
[1m141/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5466 - loss: 1.0968
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[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5517 - loss: 1.0847 - val_accuracy: 0.5316 - val_loss: 1.1158
Epoch 9/25

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[1m 29/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5526 - loss: 1.0482 
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[1m298/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5705 - loss: 1.0385
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Epoch 10/25

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Epoch 11/25

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[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.6136 - loss: 0.9515 - val_accuracy: 0.5474 - val_loss: 1.1172
Epoch 12/25

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[1m 27/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6261 - loss: 0.9188 
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[1m114/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6343 - loss: 0.9065
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[1m295/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6342 - loss: 0.9039
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Epoch 13/25

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Epoch 14/25

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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m20s[0m 422ms/step
[1m48/49[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step   2025-11-08 10:11:17.537033: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_141', 8 bytes spill stores, 8 bytes spill loads

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Saved model to disk.
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Loaded model from disk.
(1545, 6, 250)
(10469, 6, 250)

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[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m54/89[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 957us/step
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Global accuracy score (validation) = 54.85 [%]
Global F1 score (validation) = 54.06 [%]
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[[0.22214565 0.1488334  0.5608957  0.06812526]
 [0.22227374 0.1546714  0.5717879  0.05126696]
 [0.21629365 0.15443987 0.5757169  0.05354955]
 ...
 [0.05537439 0.03937864 0.06015757 0.84508944]
 [0.23788911 0.17960563 0.51718783 0.06531738]
 [0.01461107 0.01386132 0.01657702 0.95495063]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 67.19 [%]
Global accuracy score (test) = 52.1 [%]
Global F1 score (train) = 67.45 [%]
Global F1 score (test) = 52.33 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.39      0.39      0.39       400
MODERATE-INTENSITY       0.45      0.47      0.46       400
         SEDENTARY       0.61      0.69      0.65       400
VIGOROUS-INTENSITY       0.68      0.53      0.60       345

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


Accuracy capturado en la ejecución 6: 52.1 [%]
F1-score capturado en la ejecución 6: 52.33 [%]

=== EJECUCIÓN 7 ===

--- TRAIN (ejecución 7) ---

--- TEST (ejecución 7) ---
2025-11-08 10:11:28.680403: 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 10:11:28.691621: 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 00:00:1762593088.704574   64652 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762593088.708787   64652 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762593088.718803   64652 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762593088.718820   64652 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762593088.718822   64652 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762593088.718823   64652 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-08 10:11:28.722135: 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 00:00:1762593091.040931   64652 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 1/25
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762593093.520809   64783 service.cc:152] XLA service 0x794f08002130 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762593093.520842   64783 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-08 10:11:33.577313: 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 00:00:1762593093.895702   64783 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762593096.027632   64783 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 81/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2776 - loss: 1.8103
[1m112/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2901 - loss: 1.7793
[1m142/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2993 - loss: 1.7568
[1m172/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3059 - loss: 1.7388
[1m201/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3103 - loss: 1.7248
[1m232/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3139 - loss: 1.7117
[1m261/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3171 - loss: 1.7003
[1m291/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3203 - loss: 1.6892
[1m315/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.3228 - loss: 1.6809
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step - accuracy: 0.3240 - loss: 1.6767
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m8s[0m 12ms/step - accuracy: 0.3241 - loss: 1.6764 - val_accuracy: 0.4308 - val_loss: 1.3399
Epoch 2/25

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 21ms/step - accuracy: 0.4375 - loss: 1.2142
[1m 30/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4375 - loss: 1.3313 
[1m 54/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4331 - loss: 1.3429
[1m 84/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4307 - loss: 1.3482
[1m115/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4296 - loss: 1.3531
[1m143/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4284 - loss: 1.3571
[1m167/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4276 - loss: 1.3588
[1m196/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4268 - loss: 1.3603
[1m226/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4260 - loss: 1.3615
[1m255/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4254 - loss: 1.3621
[1m286/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4248 - loss: 1.3623
[1m315/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.4244 - loss: 1.3622
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4243 - loss: 1.3620 - val_accuracy: 0.4828 - val_loss: 1.2212
Epoch 3/25

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.4688 - loss: 1.1749
[1m 31/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4358 - loss: 1.2944 
[1m 63/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4281 - loss: 1.3038
[1m 94/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4282 - loss: 1.3055
[1m125/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4287 - loss: 1.3052
[1m154/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4298 - loss: 1.3049
[1m181/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4305 - loss: 1.3045
[1m210/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4309 - loss: 1.3038
[1m239/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4312 - loss: 1.3029
[1m269/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4315 - loss: 1.3021
[1m298/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4319 - loss: 1.3011
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[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4325 - loss: 1.2996 - val_accuracy: 0.4881 - val_loss: 1.1889
Epoch 4/25

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Epoch 5/25

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Epoch 6/25

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Epoch 7/25

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Epoch 8/25

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[1m297/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5624 - loss: 1.0563
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Epoch 9/25

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[1m118/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5878 - loss: 1.0215
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[1m272/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5871 - loss: 1.0185
[1m302/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5869 - loss: 1.0185
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5869 - loss: 1.0182 - val_accuracy: 0.5327 - val_loss: 1.1183
Epoch 10/25

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[1m 28/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5916 - loss: 1.0322 
[1m 59/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5951 - loss: 1.0080
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[1m118/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5926 - loss: 0.9931
[1m147/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5911 - loss: 0.9918
[1m175/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5906 - loss: 0.9913
[1m204/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5909 - loss: 0.9902
[1m232/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5912 - loss: 0.9892
[1m261/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5915 - loss: 0.9881
[1m291/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5920 - loss: 0.9869
[1m322/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.5927 - loss: 0.9856
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5929 - loss: 0.9853 - val_accuracy: 0.5358 - val_loss: 1.1233
Epoch 11/25

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[1m 30/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5700 - loss: 1.0104 
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Epoch 12/25

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Epoch 13/25

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.7500 - loss: 0.7370
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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m20s[0m 428ms/step2025-11-08 10:11:49.961834: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_141', 8 bytes spill stores, 8 bytes spill loads


[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 20ms/step  
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[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)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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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)
Loaded model from disk.
(1545, 6, 250)
(10469, 6, 250)

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[1m60/89[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 864us/step
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step  
Global accuracy score (validation) = 53.69 [%]
Global F1 score (validation) = 52.63 [%]
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[[0.15728725 0.09154458 0.20753342 0.54363483]
 [0.2007615  0.14408743 0.5105636  0.14458744]
 [0.19842714 0.14080338 0.5252666  0.13550292]
 ...
 [0.11242579 0.08207484 0.06553125 0.7399681 ]
 [0.19444942 0.10564876 0.3824854  0.31741646]
 [0.03428028 0.0230601  0.01656404 0.9260956 ]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 67.64 [%]
Global accuracy score (test) = 49.45 [%]
Global F1 score (train) = 67.09 [%]
Global F1 score (test) = 49.29 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.37      0.32      0.34       400
MODERATE-INTENSITY       0.45      0.43      0.44       400
         SEDENTARY       0.68      0.62      0.65       400
VIGOROUS-INTENSITY       0.47      0.62      0.54       345

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


Accuracy capturado en la ejecución 7: 49.45 [%]
F1-score capturado en la ejecución 7: 49.29 [%]

=== EJECUCIÓN 8 ===

--- TRAIN (ejecución 8) ---

--- TEST (ejecución 8) ---
2025-11-08 10:12:00.966068: 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 10:12:00.977589: 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 00:00:1762593120.990592   66886 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762593120.994744   66886 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762593121.004737   66886 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762593121.004756   66886 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762593121.004757   66886 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762593121.004758   66886 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-08 10:12:01.007930: 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 00:00:1762593123.352460   66886 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 1/25
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762593125.853261   67020 service.cc:152] XLA service 0x7ba288015810 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762593125.853308   67020 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-08 10:12:05.911111: 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 00:00:1762593126.222195   67020 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762593128.347338   67020 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m23:32[0m 4s/step - accuracy: 0.1562 - loss: 2.3894
[1m 26/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1925 - loss: 2.1521  
[1m 58/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2337 - loss: 1.9930
[1m 86/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2573 - loss: 1.9207
[1m117/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2744 - loss: 1.8698
[1m146/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2867 - loss: 1.8336
[1m175/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2958 - loss: 1.8054
[1m207/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3037 - loss: 1.7793
[1m238/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3097 - loss: 1.7591
[1m268/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3145 - loss: 1.7424
[1m300/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.3186 - loss: 1.7269
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step - accuracy: 0.3218 - loss: 1.7149
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m8s[0m 12ms/step - accuracy: 0.3219 - loss: 1.7145 - val_accuracy: 0.4526 - val_loss: 1.2926
Epoch 2/25

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[1m 30/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3892 - loss: 1.4207 
[1m 60/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3955 - loss: 1.4194
[1m 86/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3973 - loss: 1.4182
[1m117/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3985 - loss: 1.4147
[1m146/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3994 - loss: 1.4106
[1m176/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4000 - loss: 1.4076
[1m205/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3998 - loss: 1.4061
[1m234/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3996 - loss: 1.4047
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Epoch 3/25

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[1m184/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4117 - loss: 1.3329
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[1m245/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4127 - loss: 1.3306
[1m274/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4133 - loss: 1.3292
[1m305/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4140 - loss: 1.3275
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Epoch 4/25

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[1m240/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4606 - loss: 1.2373
[1m271/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4604 - loss: 1.2382
[1m303/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4604 - loss: 1.2386
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Epoch 5/25

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[1m114/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4370 - loss: 1.2463
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[1m176/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4468 - loss: 1.2370
[1m209/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4505 - loss: 1.2330
[1m240/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4537 - loss: 1.2295
[1m268/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4561 - loss: 1.2268
[1m299/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4583 - loss: 1.2242
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4600 - loss: 1.2222 - val_accuracy: 0.5225 - val_loss: 1.1222
Epoch 6/25

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[1m 27/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4887 - loss: 1.1643 
[1m 58/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4899 - loss: 1.1735
[1m 90/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4877 - loss: 1.1801
[1m123/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4867 - loss: 1.1820
[1m152/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4866 - loss: 1.1822
[1m182/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4871 - loss: 1.1811
[1m212/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4878 - loss: 1.1800
[1m242/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4885 - loss: 1.1786
[1m271/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4892 - loss: 1.1772
[1m302/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4901 - loss: 1.1756
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4909 - loss: 1.1740 - val_accuracy: 0.5176 - val_loss: 1.1438
Epoch 7/25

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[1m 32/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5123 - loss: 1.1300 
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[1m294/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5185 - loss: 1.1236
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Epoch 8/25

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[1m298/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5466 - loss: 1.0788
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Epoch 9/25

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[1m118/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5731 - loss: 1.0362
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[1m181/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5714 - loss: 1.0374
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[1m241/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5705 - loss: 1.0381
[1m271/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5704 - loss: 1.0378
[1m299/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5703 - loss: 1.0375
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Epoch 10/25

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4688 - loss: 1.1998
[1m 32/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5729 - loss: 1.0312 
[1m 62/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5670 - loss: 1.0308
[1m 92/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5669 - loss: 1.0281
[1m120/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5671 - loss: 1.0271
[1m152/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5683 - loss: 1.0245
[1m185/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5704 - loss: 1.0205
[1m215/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5717 - loss: 1.0173
[1m246/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5727 - loss: 1.0151
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Epoch 11/25

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Epoch 12/25

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Epoch 13/25

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[1m287/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6417 - loss: 0.8747
[1m320/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.6406 - loss: 0.8766
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Epoch 14/25

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Saved model to disk.
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Loaded model from disk.
(1545, 6, 250)
(10469, 6, 250)

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6:57[0m 1s/step
[1m 60/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 857us/step
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[1m181/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 841us/step
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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 16ms/step
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[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m57/89[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 900us/step
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step  
Global accuracy score (validation) = 54.6 [%]
Global F1 score (validation) = 53.49 [%]
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[[0.2240417  0.1624608  0.5613698  0.05212769]
 [0.0321052  0.9497028  0.01581997 0.00237204]
 [0.2553063  0.15220147 0.47536758 0.1171246 ]
 ...
 [0.07419976 0.02249155 0.0407032  0.86260545]
 [0.25904417 0.09382057 0.12376923 0.5233661 ]
 [0.03934871 0.01408935 0.0199883  0.9265737 ]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 67.67 [%]
Global accuracy score (test) = 50.74 [%]
Global F1 score (train) = 67.47 [%]
Global F1 score (test) = 50.87 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.40      0.34      0.37       400
MODERATE-INTENSITY       0.42      0.49      0.46       400
         SEDENTARY       0.66      0.61      0.64       400
VIGOROUS-INTENSITY       0.55      0.59      0.57       345

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


Accuracy capturado en la ejecución 8: 50.74 [%]
F1-score capturado en la ejecución 8: 50.87 [%]

=== EJECUCIÓN 9 ===

--- TRAIN (ejecución 9) ---

--- TEST (ejecución 9) ---
2025-11-08 10:12:33.901730: 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 10:12:33.913048: 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 00:00:1762593153.927078   69192 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762593153.931574   69192 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762593153.941760   69192 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762593153.941782   69192 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762593153.941784   69192 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762593153.941785   69192 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-08 10:12:33.945001: 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 00:00:1762593156.278956   69192 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 1/25
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762593158.804711   69330 service.cc:152] XLA service 0x7362980150b0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762593158.804745   69330 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-08 10:12:38.861656: 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 00:00:1762593159.171346   69330 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762593161.331076   69330 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m23:53[0m 4s/step - accuracy: 0.2188 - loss: 2.0222
[1m 23/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2750 - loss: 1.9222  
[1m 55/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2967 - loss: 1.8348
[1m 83/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3090 - loss: 1.7961
[1m112/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3182 - loss: 1.7679
[1m144/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3240 - loss: 1.7471
[1m175/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3278 - loss: 1.7295
[1m206/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3311 - loss: 1.7144
[1m237/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3340 - loss: 1.7010
[1m265/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3361 - loss: 1.6908
[1m294/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3384 - loss: 1.6810
[1m326/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.3409 - loss: 1.6702
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Epoch 2/25

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[1m 31/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3878 - loss: 1.4497 
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[1m 91/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3924 - loss: 1.4433
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[1m209/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3976 - loss: 1.4254
[1m239/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3994 - loss: 1.4200
[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4013 - loss: 1.4150
[1m299/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4027 - loss: 1.4110
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Epoch 3/25

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[1m 26/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4294 - loss: 1.2887 
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[1m114/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4355 - loss: 1.2864
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[1m324/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.4449 - loss: 1.2769
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4450 - loss: 1.2767 - val_accuracy: 0.5063 - val_loss: 1.1633
Epoch 4/25

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[1m 29/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4653 - loss: 1.2477 
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[1m119/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4776 - loss: 1.2198
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[1m181/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4783 - loss: 1.2175
[1m213/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4788 - loss: 1.2173
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[1m274/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4799 - loss: 1.2172
[1m303/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4804 - loss: 1.2169
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4807 - loss: 1.2165 - val_accuracy: 0.5116 - val_loss: 1.1605
Epoch 5/25

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[1m 29/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5141 - loss: 1.1840 
[1m 59/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5064 - loss: 1.1972
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[1m148/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5046 - loss: 1.1915
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Epoch 6/25

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Epoch 7/25

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Epoch 8/25

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[1m 28/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5559 - loss: 1.0640 
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[1m114/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5721 - loss: 1.0390
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[1m293/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5777 - loss: 1.0353
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Epoch 9/25

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[1m 29/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6010 - loss: 0.9603 
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Epoch 10/25

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Epoch 11/25

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[1m288/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6274 - loss: 0.9128
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Epoch 12/25

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[1m 29/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6325 - loss: 0.9021 
[1m 58/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6416 - loss: 0.8893
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[1m117/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6496 - loss: 0.8780
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[1m261/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6546 - loss: 0.8699
[1m293/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6549 - loss: 0.8695
[1m324/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.6550 - loss: 0.8695
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.6550 - loss: 0.8695 - val_accuracy: 0.5632 - val_loss: 1.1328
Epoch 13/25

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[1m 30/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6181 - loss: 0.9056 
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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m20s[0m 428ms/step2025-11-08 10:12:55.229876: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_141', 8 bytes spill stores, 8 bytes spill loads

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Saved model to disk.
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Loaded model from disk.
(1545, 6, 250)
(10469, 6, 250)

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6:58[0m 1s/step
[1m 55/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 929us/step
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[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) = 53.3 [%]
Global F1 score (validation) = 52.4 [%]
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[[0.19635692 0.10687114 0.50106394 0.19570795]
 [0.49950162 0.20357703 0.20156237 0.09535898]
 [0.18506436 0.10769062 0.55276155 0.15448336]
 ...
 [0.0456479  0.00813059 0.01380663 0.93241495]
 [0.17544235 0.09693651 0.17652348 0.5510977 ]
 [0.06665451 0.01673799 0.0185988  0.89800876]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 62.24 [%]
Global accuracy score (test) = 50.68 [%]
Global F1 score (train) = 61.47 [%]
Global F1 score (test) = 50.28 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.43      0.30      0.35       400
MODERATE-INTENSITY       0.46      0.56      0.51       400
         SEDENTARY       0.66      0.56      0.61       400
VIGOROUS-INTENSITY       0.49      0.62      0.55       345

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


Accuracy capturado en la ejecución 9: 50.68 [%]
F1-score capturado en la ejecución 9: 50.28 [%]

=== EJECUCIÓN 10 ===

--- TRAIN (ejecución 10) ---

--- TEST (ejecución 10) ---
2025-11-08 10:13:06.188071: 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 10:13:06.199382: 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 00:00:1762593186.212545   71431 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762593186.216473   71431 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762593186.226702   71431 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762593186.226720   71431 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762593186.226721   71431 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762593186.226722   71431 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-08 10:13:06.229913: 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 00:00:1762593188.593090   71431 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 1/25
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762593191.086888   71547 service.cc:152] XLA service 0x728244015c70 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762593191.086915   71547 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-08 10:13:11.135850: 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 00:00:1762593191.445872   71547 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762593193.600092   71547 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m23:30[0m 4s/step - accuracy: 0.1875 - loss: 1.8168
[1m 26/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2687 - loss: 1.7770  
[1m 53/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2830 - loss: 1.7503
[1m 80/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2917 - loss: 1.7275
[1m111/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2998 - loss: 1.7060
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Epoch 2/25

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Epoch 3/25

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[1m119/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4403 - loss: 1.2806
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[1m180/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4352 - loss: 1.2874
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[1m269/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4327 - loss: 1.2910
[1m298/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4325 - loss: 1.2913
[1m324/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.4326 - loss: 1.2911
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4326 - loss: 1.2910 - val_accuracy: 0.4793 - val_loss: 1.1876
Epoch 4/25

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[1m 28/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4840 - loss: 1.2374 
[1m 59/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4782 - loss: 1.2353
[1m 85/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4750 - loss: 1.2353
[1m113/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4719 - loss: 1.2355
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[1m173/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4676 - loss: 1.2366
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[1m289/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4643 - loss: 1.2394
[1m318/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.4640 - loss: 1.2396
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Epoch 5/25

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[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4766 - loss: 1.2022
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Epoch 6/25

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[1m298/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5045 - loss: 1.1661
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Epoch 7/25

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[1m122/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5497 - loss: 1.0975
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[1m183/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5432 - loss: 1.1032
[1m213/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5415 - loss: 1.1049
[1m242/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5406 - loss: 1.1058
[1m273/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5400 - loss: 1.1061
[1m300/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5396 - loss: 1.1061
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5395 - loss: 1.1059 - val_accuracy: 0.5302 - val_loss: 1.1256
Epoch 8/25

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[1m 32/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5493 - loss: 1.0788 
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[1m124/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5536 - loss: 1.0702
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[1m182/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5543 - loss: 1.0667
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[1m241/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5536 - loss: 1.0663
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Epoch 9/25

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Epoch 10/25

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Epoch 11/25

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Epoch 12/25

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[1m 27/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5680 - loss: 1.0239 
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Epoch 13/25

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Epoch 14/25

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Epoch 15/25

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Epoch 16/25

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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m21s[0m 438ms/step2025-11-08 10:13:29.698100: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_141', 8 bytes spill stores, 8 bytes spill loads

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Saved model to disk.
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Loaded model from disk.
(1545, 6, 250)
(10469, 6, 250)

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6:38[0m 1s/step
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[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m51/89[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step 
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step
Global accuracy score (validation) = 56.14 [%]
Global F1 score (validation) = 55.35 [%]
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[[0.15415575 0.11446369 0.6814535  0.04992701]
 [0.12369984 0.1148905  0.73794895 0.0234607 ]
 [0.39372233 0.4799896  0.1120865  0.01420157]
 ...
 [0.23728529 0.07846759 0.29091012 0.39333698]
 [0.31817392 0.13721032 0.32774284 0.21687298]
 [0.25886708 0.13513376 0.46962348 0.1363757 ]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 69.27 [%]
Global accuracy score (test) = 52.75 [%]
Global F1 score (train) = 69.49 [%]
Global F1 score (test) = 52.44 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.39      0.32      0.35       400
MODERATE-INTENSITY       0.47      0.53      0.50       400
         SEDENTARY       0.59      0.72      0.65       400
VIGOROUS-INTENSITY       0.67      0.55      0.60       345

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


Accuracy capturado en la ejecución 10: 52.75 [%]
F1-score capturado en la ejecución 10: 52.44 [%]

=== EJECUCIÓN 11 ===

--- TRAIN (ejecución 11) ---

--- TEST (ejecución 11) ---
2025-11-08 10:13:40.583756: 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 10:13:40.594964: 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 00:00:1762593220.607887   73949 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762593220.611937   73949 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762593220.621586   73949 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762593220.621601   73949 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762593220.621602   73949 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762593220.621603   73949 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-08 10:13:40.624762: 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 00:00:1762593222.942058   73949 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 1/25
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762593225.426199   74059 service.cc:152] XLA service 0x71c85c003230 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762593225.426243   74059 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-08 10:13:45.480646: 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 00:00:1762593225.805890   74059 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762593227.967588   74059 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m23:43[0m 4s/step - accuracy: 0.4062 - loss: 1.7884
[1m 24/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3007 - loss: 1.8371  
[1m 52/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3029 - loss: 1.8178
[1m 83/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3064 - loss: 1.7943
[1m114/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3107 - loss: 1.7742
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[1m169/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3168 - loss: 1.7447
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Epoch 2/25

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[1m293/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3987 - loss: 1.3788
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Epoch 3/25

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[1m123/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4453 - loss: 1.2865
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[1m183/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4451 - loss: 1.2846
[1m211/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4443 - loss: 1.2848
[1m240/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4438 - loss: 1.2844
[1m269/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4433 - loss: 1.2840
[1m295/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4428 - loss: 1.2839
[1m324/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.4423 - loss: 1.2839
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4422 - loss: 1.2839 - val_accuracy: 0.4779 - val_loss: 1.2002
Epoch 4/25

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[1m 26/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4388 - loss: 1.2468 
[1m 58/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4427 - loss: 1.2479
[1m 89/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4449 - loss: 1.2469
[1m118/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4464 - loss: 1.2451
[1m147/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4473 - loss: 1.2436
[1m179/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4481 - loss: 1.2425
[1m209/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4484 - loss: 1.2418
[1m238/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4486 - loss: 1.2413
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[1m294/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4491 - loss: 1.2398
[1m322/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.4494 - loss: 1.2392
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Epoch 5/25

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[1m 27/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4760 - loss: 1.2317 
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Epoch 6/25

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Epoch 7/25

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[1m290/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5281 - loss: 1.1096
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[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5273 - loss: 1.1109 - val_accuracy: 0.5256 - val_loss: 1.1214
Epoch 8/25

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[1m 31/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5626 - loss: 1.0549 
[1m 60/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5544 - loss: 1.0691
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[1m118/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5453 - loss: 1.0853
[1m149/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5425 - loss: 1.0902
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Epoch 9/25

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Epoch 10/25

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Epoch 11/25

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Epoch 12/25

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Epoch 13/25

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Epoch 14/25

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[1m271/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6678 - loss: 0.8281
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Epoch 15/25

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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m20s[0m 425ms/step
[1m48/49[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step   2025-11-08 10:14:03.328848: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_141', 8 bytes spill stores, 8 bytes spill loads

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Saved model to disk.
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Loaded model from disk.
(1545, 6, 250)
(10469, 6, 250)

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6:45[0m 1s/step
[1m 52/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 982us/step
[1m115/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 883us/step
[1m174/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 873us/step
[1m238/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 851us/step
[1m299/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 846us/step
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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 17ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 18ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 18ms/step

[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m55/89[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 935us/step
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step  
Global accuracy score (validation) = 55.48 [%]
Global F1 score (validation) = 54.44 [%]
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[[0.11507218 0.11399887 0.75418735 0.01674152]
 [0.10833574 0.10866174 0.7714722  0.01153033]
 [0.10807239 0.108739   0.77128077 0.01190779]
 ...
 [0.20484975 0.1506362  0.30791903 0.33659506]
 [0.26306915 0.13485362 0.49125636 0.11082086]
 [0.13206749 0.10951786 0.06695433 0.6914603 ]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 70.36 [%]
Global accuracy score (test) = 52.36 [%]
Global F1 score (train) = 70.77 [%]
Global F1 score (test) = 52.15 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.40      0.37      0.38       400
MODERATE-INTENSITY       0.45      0.45      0.45       400
         SEDENTARY       0.58      0.76      0.66       400
VIGOROUS-INTENSITY       0.69      0.52      0.60       345

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


Accuracy capturado en la ejecución 11: 52.36 [%]
F1-score capturado en la ejecución 11: 52.15 [%]

=== EJECUCIÓN 12 ===

--- TRAIN (ejecución 12) ---

--- TEST (ejecución 12) ---
2025-11-08 10:14:14.418538: 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 10:14:14.429914: 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 00:00:1762593254.443146   76349 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762593254.447450   76349 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762593254.457554   76349 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762593254.457576   76349 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762593254.457577   76349 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762593254.457578   76349 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-08 10:14:14.461008: 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 00:00:1762593256.809857   76349 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 1/25
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762593259.353850   76487 service.cc:152] XLA service 0x7ec338002bd0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762593259.353920   76487 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-08 10:14:19.406463: 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 00:00:1762593259.728737   76487 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762593261.860236   76487 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m23:43[0m 4s/step - accuracy: 0.1875 - loss: 1.7199
[1m 22/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2581 - loss: 1.9224  
[1m 53/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2778 - loss: 1.8750
[1m 84/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2919 - loss: 1.8385
[1m116/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2994 - loss: 1.8116
[1m146/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3065 - loss: 1.7866
[1m177/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3127 - loss: 1.7650
[1m208/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3183 - loss: 1.7463
[1m239/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3227 - loss: 1.7307
[1m266/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3258 - loss: 1.7189
[1m292/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3282 - loss: 1.7088
[1m322/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.3307 - loss: 1.6985
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step - accuracy: 0.3312 - loss: 1.6965
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Epoch 2/25

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4062 - loss: 1.2907
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[1m 90/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4022 - loss: 1.4100
[1m120/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4016 - loss: 1.4121
[1m154/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4016 - loss: 1.4114
[1m186/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4020 - loss: 1.4095
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[1m278/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4030 - loss: 1.4037
[1m307/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4036 - loss: 1.4017
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Epoch 3/25

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[1m 27/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3952 - loss: 1.3584 
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[1m294/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4213 - loss: 1.3238
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Epoch 4/25

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[1m180/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4597 - loss: 1.2433
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[1m269/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4580 - loss: 1.2454
[1m297/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4577 - loss: 1.2456
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Epoch 5/25

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[1m 32/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4772 - loss: 1.2001 
[1m 62/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4797 - loss: 1.1986
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[1m119/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4827 - loss: 1.1978
[1m151/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4830 - loss: 1.1980
[1m181/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4831 - loss: 1.1991
[1m211/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4835 - loss: 1.1993
[1m240/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4841 - loss: 1.1997
[1m271/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4847 - loss: 1.1996
[1m303/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4853 - loss: 1.1993
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Epoch 6/25

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.6875 - loss: 0.9011
[1m 30/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4904 - loss: 1.1497 
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[1m239/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5025 - loss: 1.1532
[1m268/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5033 - loss: 1.1533
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Epoch 7/25

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[1m276/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5245 - loss: 1.1234
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Epoch 8/25

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[1m124/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5495 - loss: 1.0751
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[1m244/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5500 - loss: 1.0708
[1m277/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5504 - loss: 1.0705
[1m308/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5504 - loss: 1.0706
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5504 - loss: 1.0708 - val_accuracy: 0.5379 - val_loss: 1.0841
Epoch 9/25

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[1m 32/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5605 - loss: 1.0297 
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[1m 92/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5672 - loss: 1.0333
[1m122/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5704 - loss: 1.0312
[1m154/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5724 - loss: 1.0298
[1m182/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5729 - loss: 1.0298
[1m213/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5733 - loss: 1.0301
[1m245/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5735 - loss: 1.0304
[1m273/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5734 - loss: 1.0307
[1m305/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5734 - loss: 1.0309
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5733 - loss: 1.0308 - val_accuracy: 0.5351 - val_loss: 1.0786
Epoch 10/25

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[1m 31/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5899 - loss: 0.9682 
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[1m 91/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5889 - loss: 0.9860
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Epoch 11/25

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[1m314/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.6028 - loss: 0.9601
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Epoch 12/25

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[1m220/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6091 - loss: 0.9433
[1m252/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6098 - loss: 0.9421
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[1m311/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.6108 - loss: 0.9403
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.6112 - loss: 0.9398 - val_accuracy: 0.5523 - val_loss: 1.0728
Epoch 13/25

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[1m 28/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6415 - loss: 0.8515 
[1m 58/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6416 - loss: 0.8622
[1m 86/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6425 - loss: 0.8641
[1m116/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6405 - loss: 0.8698
[1m147/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6381 - loss: 0.8756
[1m178/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6364 - loss: 0.8790
[1m209/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6356 - loss: 0.8814
[1m238/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6354 - loss: 0.8826
[1m269/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6353 - loss: 0.8835
[1m301/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.6354 - loss: 0.8841
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.6354 - loss: 0.8847 - val_accuracy: 0.5442 - val_loss: 1.1004
Epoch 14/25

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[1m 31/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6531 - loss: 0.8417 
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Epoch 15/25

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Epoch 16/25

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Epoch 17/25

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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m20s[0m 428ms/step2025-11-08 10:14:38.514027: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_141', 8 bytes spill stores, 8 bytes spill loads


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Saved model to disk.
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
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Loaded model from disk.
(1545, 6, 250)
(10469, 6, 250)

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7:02[0m 1s/step
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[1m157/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 970us/step
[1m209/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 969us/step
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[1m322/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 940us/step
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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 17ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 18ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 18ms/step

[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 15ms/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) = 53.55 [%]
Global F1 score (validation) = 52.83 [%]
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[[0.23240763 0.1343861  0.55047596 0.08273038]
 [0.23578538 0.139171   0.5416529  0.08339075]
 [0.22991967 0.13492137 0.5577996  0.07735941]
 ...
 [0.12853076 0.03918479 0.06209978 0.7701847 ]
 [0.19074716 0.0360644  0.0525183  0.72067016]
 [0.35003874 0.1240942  0.2882335  0.2376336 ]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 69.93 [%]
Global accuracy score (test) = 53.01 [%]
Global F1 score (train) = 70.13 [%]
Global F1 score (test) = 53.27 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.39      0.53      0.45       400
MODERATE-INTENSITY       0.54      0.38      0.45       400
         SEDENTARY       0.63      0.66      0.65       400
VIGOROUS-INTENSITY       0.63      0.55      0.59       345

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


Accuracy capturado en la ejecución 12: 53.01 [%]
F1-score capturado en la ejecución 12: 53.27 [%]

=== EJECUCIÓN 13 ===

--- TRAIN (ejecución 13) ---

--- TEST (ejecución 13) ---
2025-11-08 10:14:49.456129: 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 10:14:49.467393: 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 00:00:1762593289.480684   78970 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762593289.484587   78970 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762593289.494459   78970 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762593289.494475   78970 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762593289.494476   78970 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762593289.494477   78970 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-08 10:14:49.497565: 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 00:00:1762593291.823827   78970 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 1/25
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762593294.382747   79089 service.cc:152] XLA service 0x71ebf4015980 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762593294.382796   79089 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-08 10:14:54.437605: 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 00:00:1762593294.744946   79089 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762593296.873274   79089 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m23:44[0m 4s/step - accuracy: 0.3750 - loss: 1.9418
[1m 23/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2807 - loss: 1.8325  
[1m 54/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2942 - loss: 1.7777
[1m 84/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3041 - loss: 1.7402
[1m116/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3135 - loss: 1.7116
[1m146/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3192 - loss: 1.6937
[1m174/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3232 - loss: 1.6799
[1m203/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3270 - loss: 1.6681
[1m231/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3304 - loss: 1.6577
[1m262/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3334 - loss: 1.6479
[1m293/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3363 - loss: 1.6386
[1m319/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.3386 - loss: 1.6312
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step - accuracy: 0.3393 - loss: 1.6288
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Epoch 2/25

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[1m 60/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4087 - loss: 1.4083
[1m 90/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4103 - loss: 1.3999
[1m122/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4126 - loss: 1.3951
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[1m215/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4168 - loss: 1.3876
[1m245/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4169 - loss: 1.3860
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Epoch 3/25

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[1m232/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4443 - loss: 1.2937
[1m261/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4444 - loss: 1.2926
[1m291/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4447 - loss: 1.2913
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[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4450 - loss: 1.2898 - val_accuracy: 0.4796 - val_loss: 1.1877
Epoch 4/25

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[1m116/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4468 - loss: 1.2487
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[1m266/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4510 - loss: 1.2447
[1m297/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4516 - loss: 1.2442
[1m327/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.4524 - loss: 1.2436
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4525 - loss: 1.2435 - val_accuracy: 0.4772 - val_loss: 1.1579
Epoch 5/25

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4688 - loss: 1.1956
[1m 32/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4978 - loss: 1.1524 
[1m 62/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4905 - loss: 1.1752
[1m 92/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4837 - loss: 1.1890
[1m123/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4805 - loss: 1.1961
[1m154/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4796 - loss: 1.1984
[1m183/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4797 - loss: 1.1985
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[1m275/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4809 - loss: 1.1968
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Epoch 6/25

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[1m 28/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5108 - loss: 1.1305 
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Epoch 7/25

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[1m298/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5372 - loss: 1.1065
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Epoch 8/25

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[1m296/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5568 - loss: 1.0710
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Epoch 9/25

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[1m 28/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6142 - loss: 0.9666 
[1m 57/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5998 - loss: 0.9758
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[1m112/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5872 - loss: 0.9911
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[1m170/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5832 - loss: 0.9973
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[1m295/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5805 - loss: 1.0044
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Epoch 10/25

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Epoch 11/25

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Epoch 12/25

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[1m301/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.6246 - loss: 0.9128
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Epoch 13/25

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Epoch 14/25

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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m19s[0m 416ms/step2025-11-08 10:15:11.497190: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_141', 8 bytes spill stores, 8 bytes spill loads

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Saved model to disk.
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Loaded model from disk.
(1545, 6, 250)
(10469, 6, 250)

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6:50[0m 1s/step
[1m 61/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 836us/step
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[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 15ms/step
[1m56/89[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 921us/step
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step  
Global accuracy score (validation) = 55.2 [%]
Global F1 score (validation) = 55.09 [%]
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[[0.22453833 0.11775463 0.5806533  0.07705376]
 [0.26746783 0.10250033 0.46697384 0.16305797]
 [0.26351157 0.1047942  0.4753892  0.15630497]
 ...
 [0.14953318 0.01732084 0.02233791 0.81080806]
 [0.297095   0.06146247 0.11396735 0.5274753 ]
 [0.03959699 0.0030857  0.0035901  0.9537271 ]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 71.5 [%]
Global accuracy score (test) = 51.78 [%]
Global F1 score (train) = 71.38 [%]
Global F1 score (test) = 52.22 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.39      0.38      0.38       400
MODERATE-INTENSITY       0.45      0.57      0.50       400
         SEDENTARY       0.70      0.59      0.64       400
VIGOROUS-INTENSITY       0.59      0.54      0.57       345

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


Accuracy capturado en la ejecución 13: 51.78 [%]
F1-score capturado en la ejecución 13: 52.22 [%]

=== EJECUCIÓN 14 ===

--- TRAIN (ejecución 14) ---

--- TEST (ejecución 14) ---
2025-11-08 10:15:22.382334: 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 10:15:22.393634: 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 00:00:1762593322.406930   81302 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762593322.411093   81302 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762593322.420762   81302 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762593322.420779   81302 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762593322.420780   81302 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762593322.420782   81302 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-08 10:15:22.423864: 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 00:00:1762593324.746114   81302 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 1/25
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762593327.233491   81413 service.cc:152] XLA service 0x7202e4014f50 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762593327.233520   81413 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-08 10:15:27.284756: 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 00:00:1762593327.591850   81413 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762593329.745874   81413 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 2/25

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[1m258/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3996 - loss: 1.3947
[1m285/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4005 - loss: 1.3925
[1m316/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.4016 - loss: 1.3901
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Epoch 3/25

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[1m124/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4281 - loss: 1.3168
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[1m183/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4298 - loss: 1.3131
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[1m242/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4307 - loss: 1.3103
[1m274/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4313 - loss: 1.3088
[1m304/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4318 - loss: 1.3075
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Epoch 4/25

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Epoch 5/25

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Epoch 6/25

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Epoch 7/25

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Epoch 8/25

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 23ms/step - accuracy: 0.6875 - loss: 1.0634
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Epoch 9/25

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[1m310/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5827 - loss: 1.0251
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Epoch 10/25

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[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6000 - loss: 0.9854
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Epoch 11/25

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[1m 32/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6072 - loss: 0.9755 
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[1m 91/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6084 - loss: 0.9674
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[1m182/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6072 - loss: 0.9675
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[1m247/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6074 - loss: 0.9669
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Epoch 12/25

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[1m 30/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6242 - loss: 0.9542 
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Epoch 13/25

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Epoch 14/25

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Epoch 15/25

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Epoch 16/25

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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m21s[0m 440ms/step2025-11-08 10:15:45.759665: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_141', 8 bytes spill stores, 8 bytes spill loads

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Saved model to disk.
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Loaded model from disk.
(1545, 6, 250)
(10469, 6, 250)

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6:26[0m 1s/step
[1m 43/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step 
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[1m163/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 937us/step
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[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 18ms/step
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[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m51/89[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step 
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step
Global accuracy score (validation) = 53.97 [%]
Global F1 score (validation) = 53.23 [%]
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[[0.22115867 0.13636178 0.4952237  0.14725581]
 [0.27588877 0.1575952  0.51424843 0.05226755]
 [0.19315657 0.13962054 0.6067394  0.06048352]
 ...
 [0.21035112 0.08869179 0.10377613 0.59718096]
 [0.04262932 0.01550096 0.01999765 0.921872  ]
 [0.3954427  0.15744211 0.16692086 0.2801943 ]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 68.74 [%]
Global accuracy score (test) = 52.49 [%]
Global F1 score (train) = 68.8 [%]
Global F1 score (test) = 53.08 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.37      0.39      0.38       400
MODERATE-INTENSITY       0.47      0.47      0.47       400
         SEDENTARY       0.63      0.64      0.63       400
VIGOROUS-INTENSITY       0.68      0.61      0.64       345

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


Accuracy capturado en la ejecución 14: 52.49 [%]
F1-score capturado en la ejecución 14: 53.08 [%]

=== EJECUCIÓN 15 ===

--- TRAIN (ejecución 15) ---

--- TEST (ejecución 15) ---
2025-11-08 10:15:56.673389: 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 10:15:56.684832: 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 00:00:1762593356.698246   83792 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762593356.702356   83792 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762593356.712151   83792 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762593356.712168   83792 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762593356.712169   83792 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762593356.712170   83792 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-08 10:15:56.715302: 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 00:00:1762593359.054381   83792 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 1/25
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762593361.606200   83921 service.cc:152] XLA service 0x787df0008460 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762593361.606247   83921 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-08 10:16:01.659817: 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 00:00:1762593361.967854   83921 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762593364.103445   83921 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m23:43[0m 4s/step - accuracy: 0.2500 - loss: 1.6957
[1m 25/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3176 - loss: 1.7110  
[1m 54/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3237 - loss: 1.7032
[1m 86/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3285 - loss: 1.6907
[1m115/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3325 - loss: 1.6792
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Epoch 2/25

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[1m271/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3974 - loss: 1.4024
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Epoch 3/25

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[1m299/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4322 - loss: 1.3020
[1m326/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.4321 - loss: 1.3014
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Epoch 4/25

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[1m 59/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4364 - loss: 1.2449
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[1m121/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4360 - loss: 1.2526
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[1m239/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4403 - loss: 1.2531
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[1m298/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4425 - loss: 1.2522
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Epoch 5/25

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Epoch 6/25

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Epoch 7/25

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[1m289/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5330 - loss: 1.1124
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[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5327 - loss: 1.1129 - val_accuracy: 0.5351 - val_loss: 1.1201
Epoch 8/25

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4062 - loss: 1.2094
[1m 31/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5496 - loss: 1.0659 
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[1m 92/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5512 - loss: 1.0757
[1m123/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5528 - loss: 1.0756
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[1m181/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5546 - loss: 1.0741
[1m209/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5547 - loss: 1.0740
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[1m299/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5539 - loss: 1.0748
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Epoch 9/25

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Epoch 10/25

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Epoch 11/25

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Epoch 12/25

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Epoch 13/25

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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m19s[0m 416ms/step2025-11-08 10:16:17.959462: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_141', 8 bytes spill stores, 8 bytes spill loads

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Saved model to disk.
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Loaded model from disk.
(1545, 6, 250)
(10469, 6, 250)

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6:41[0m 1s/step
[1m 59/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 866us/step
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[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m58/89[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 882us/step
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 995us/step
Global accuracy score (validation) = 56.25 [%]
Global F1 score (validation) = 55.32 [%]
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[[0.10782216 0.1850822  0.68377924 0.02331642]
 [0.13404904 0.15903983 0.66484076 0.0420703 ]
 [0.20429514 0.43666068 0.28118297 0.07786112]
 ...
 [0.18996319 0.03617754 0.08072111 0.6931381 ]
 [0.18937808 0.26765043 0.4999383  0.04303315]
 [0.17108887 0.13816872 0.05524273 0.6354997 ]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 71.33 [%]
Global accuracy score (test) = 53.2 [%]
Global F1 score (train) = 71.55 [%]
Global F1 score (test) = 52.6 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.39      0.27      0.32       400
MODERATE-INTENSITY       0.44      0.59      0.51       400
         SEDENTARY       0.61      0.73      0.67       400
VIGOROUS-INTENSITY       0.71      0.54      0.61       345

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


Accuracy capturado en la ejecución 15: 53.2 [%]
F1-score capturado en la ejecución 15: 52.6 [%]

=== EJECUCIÓN 16 ===

--- TRAIN (ejecución 16) ---

--- TEST (ejecución 16) ---
2025-11-08 10:16:28.922988: 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 10:16:28.934242: 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 00:00:1762593388.947359   86024 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762593388.951429   86024 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762593388.961304   86024 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762593388.961320   86024 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762593388.961321   86024 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762593388.961322   86024 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-08 10:16:28.964432: 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 00:00:1762593391.285854   86024 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13758 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
Epoch 1/25
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762593393.782823   86132 service.cc:152] XLA service 0x73d134026fc0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762593393.782849   86132 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-08 10:16:33.832759: 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 00:00:1762593394.141035   86132 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762593396.277702   86132 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 2/25

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Epoch 3/25

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Epoch 4/25

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Epoch 5/25

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Epoch 6/25

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[1m297/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4834 - loss: 1.1755
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[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4839 - loss: 1.1750 - val_accuracy: 0.5348 - val_loss: 1.1223
Epoch 7/25

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[1m 26/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5418 - loss: 1.1073 
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[1m112/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5294 - loss: 1.1239
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[1m289/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5223 - loss: 1.1260
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Epoch 8/25

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[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5362 - loss: 1.0861 - val_accuracy: 0.5337 - val_loss: 1.1129
Epoch 9/25

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.5000 - loss: 1.1174
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[1m121/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5593 - loss: 1.0405
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[1m266/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5567 - loss: 1.0430
[1m296/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5568 - loss: 1.0432
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Epoch 10/25

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[1m 30/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5829 - loss: 0.9853 
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[1m 90/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5731 - loss: 1.0049
[1m116/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5726 - loss: 1.0059
[1m145/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5731 - loss: 1.0056
[1m175/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5734 - loss: 1.0053
[1m202/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5738 - loss: 1.0048
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[1m289/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5751 - loss: 1.0033
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[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5757 - loss: 1.0027 - val_accuracy: 0.5256 - val_loss: 1.1410
Epoch 11/25

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.7500 - loss: 0.8181
[1m 27/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6173 - loss: 0.9308 
[1m 57/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6053 - loss: 0.9471
[1m 84/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6020 - loss: 0.9534
[1m112/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5991 - loss: 0.9576
[1m142/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5964 - loss: 0.9604
[1m170/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5952 - loss: 0.9620
[1m199/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5945 - loss: 0.9631
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[1m291/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5941 - loss: 0.9632
[1m324/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.5940 - loss: 0.9632
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Epoch 12/25

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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m21s[0m 439ms/step2025-11-08 10:16:49.564372: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_141', 8 bytes spill stores, 8 bytes spill loads

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Saved model to disk.
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Loaded model from disk.
(1545, 6, 250)
(10469, 6, 250)

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6:50[0m 1s/step
[1m 55/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 935us/step
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[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 18ms/step
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[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 17ms/step
[1m54/89[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 948us/step
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step  
Global accuracy score (validation) = 54.56 [%]
Global F1 score (validation) = 53.3 [%]
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[[0.1867556  0.1179781  0.6528715  0.04239484]
 [0.20280871 0.10984144 0.61158067 0.07576917]
 [0.2646476  0.21806195 0.21568404 0.3016064 ]
 ...
 [0.06313913 0.02714275 0.08084463 0.8288735 ]
 [0.20539992 0.10766275 0.4312576  0.25567973]
 [0.11325491 0.07574361 0.12560192 0.6853996 ]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 67.58 [%]
Global accuracy score (test) = 52.1 [%]
Global F1 score (train) = 67.32 [%]
Global F1 score (test) = 51.91 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.41      0.38      0.39       400
MODERATE-INTENSITY       0.45      0.40      0.43       400
         SEDENTARY       0.57      0.71      0.63       400
VIGOROUS-INTENSITY       0.65      0.61      0.63       345

          accuracy                           0.52      1545
         macro avg       0.52      0.52      0.52      1545
      weighted avg       0.52      0.52      0.52      1545


Accuracy capturado en la ejecución 16: 52.1 [%]
F1-score capturado en la ejecución 16: 51.91 [%]

=== EJECUCIÓN 17 ===

--- TRAIN (ejecución 17) ---

--- TEST (ejecución 17) ---
2025-11-08 10:17:00.565518: 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 10:17:00.576722: 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 00:00:1762593420.589695   88145 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762593420.593797   88145 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762593420.603683   88145 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762593420.603701   88145 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762593420.603703   88145 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762593420.603704   88145 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-08 10:17:00.607031: 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 00:00:1762593422.938389   88145 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 1/25
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762593425.423720   88287 service.cc:152] XLA service 0x7ddc94002720 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762593425.423746   88287 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-08 10:17:05.473553: 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 00:00:1762593425.781287   88287 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762593427.934403   88287 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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[1m318/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.3315 - loss: 1.6567
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Epoch 2/25

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[1m184/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3847 - loss: 1.4067
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[1m276/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3883 - loss: 1.3995
[1m306/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.3893 - loss: 1.3979
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Epoch 3/25

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[1m123/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4021 - loss: 1.3311
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[1m274/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4111 - loss: 1.3153
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Epoch 4/25

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Epoch 5/25

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Epoch 6/25

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[1m294/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4936 - loss: 1.1793
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Epoch 7/25

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[1m 32/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5120 - loss: 1.1473 
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[1m 91/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5161 - loss: 1.1417
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[1m178/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5175 - loss: 1.1402
[1m210/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5181 - loss: 1.1387
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[1m271/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5196 - loss: 1.1359
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Epoch 8/25

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[1m 28/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5641 - loss: 1.0657 
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Epoch 9/25

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[1m261/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5636 - loss: 1.0512
[1m289/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5641 - loss: 1.0501
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Epoch 10/25

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[1m261/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5890 - loss: 0.9985
[1m291/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5885 - loss: 0.9985
[1m318/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.5883 - loss: 0.9982
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5883 - loss: 0.9981 - val_accuracy: 0.5439 - val_loss: 1.0906
Epoch 11/25

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[1m 29/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6111 - loss: 0.9297 
[1m 60/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6143 - loss: 0.9408
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[1m120/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6109 - loss: 0.9493
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[1m297/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.6073 - loss: 0.9547
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Epoch 12/25

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[1m 29/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6148 - loss: 0.9268 
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Epoch 13/25

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Epoch 14/25

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Epoch 15/25

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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m19s[0m 410ms/step2025-11-08 10:17:23.352618: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_141', 8 bytes spill stores, 8 bytes spill loads

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Saved model to disk.
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Loaded model from disk.
(1545, 6, 250)
(10469, 6, 250)

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7:08[0m 1s/step
[1m 52/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 989us/step
[1m110/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 927us/step
[1m171/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 888us/step
[1m229/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 885us/step
[1m281/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 901us/step
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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 16ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 18ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 18ms/step

[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m54/89[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 949us/step
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step  
Global accuracy score (validation) = 52.49 [%]
Global F1 score (validation) = 51.1 [%]
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[[0.20550103 0.16056752 0.5385917  0.09533984]
 [0.25671586 0.47063258 0.2546212  0.01803035]
 [0.20512171 0.16049053 0.5458892  0.08849863]
 ...
 [0.22318237 0.16302018 0.28563616 0.32816127]
 [0.04299105 0.02503877 0.04476206 0.88720816]
 [0.08159707 0.06242962 0.10911696 0.74685633]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 65.42 [%]
Global accuracy score (test) = 52.69 [%]
Global F1 score (train) = 65.47 [%]
Global F1 score (test) = 52.63 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.38      0.34      0.36       400
MODERATE-INTENSITY       0.45      0.46      0.46       400
         SEDENTARY       0.63      0.72      0.67       400
VIGOROUS-INTENSITY       0.65      0.60      0.62       345

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


Accuracy capturado en la ejecución 17: 52.69 [%]
F1-score capturado en la ejecución 17: 52.63 [%]

=== EJECUCIÓN 18 ===

--- TRAIN (ejecución 18) ---

--- TEST (ejecución 18) ---
2025-11-08 10:17:34.334828: 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 10:17:34.346193: 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 00:00:1762593454.359623   90572 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762593454.363796   90572 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762593454.373658   90572 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762593454.373678   90572 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762593454.373680   90572 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762593454.373682   90572 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-08 10:17:34.376894: 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 00:00:1762593456.727136   90572 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 1/25
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762593459.277414   90683 service.cc:152] XLA service 0x754138026fc0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762593459.277438   90683 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-08 10:17:39.340529: 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 00:00:1762593459.653432   90683 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762593461.831692   90683 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m24:03[0m 4s/step - accuracy: 0.1562 - loss: 2.0882
[1m 25/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2461 - loss: 1.9664  
[1m 54/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2707 - loss: 1.9038
[1m 84/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2846 - loss: 1.8668
[1m115/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2952 - loss: 1.8355
[1m146/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3040 - loss: 1.8082
[1m176/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3100 - loss: 1.7885
[1m204/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3146 - loss: 1.7728
[1m234/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3194 - loss: 1.7574
[1m261/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3230 - loss: 1.7445
[1m290/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3263 - loss: 1.7319
[1m320/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.3293 - loss: 1.7199
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step - accuracy: 0.3300 - loss: 1.7169
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m8s[0m 12ms/step - accuracy: 0.3301 - loss: 1.7165 - val_accuracy: 0.3897 - val_loss: 1.4277
Epoch 2/25

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[1m 28/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3712 - loss: 1.5152 
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[1m274/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3995 - loss: 1.4161
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Epoch 3/25

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[1m291/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4391 - loss: 1.3022
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Epoch 4/25

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Epoch 5/25

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[1m 30/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5001 - loss: 1.2072 
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Epoch 6/25

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Epoch 7/25

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Epoch 8/25

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[1m 30/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5943 - loss: 1.0617 
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[1m114/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5644 - loss: 1.0817
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[1m290/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5614 - loss: 1.0776
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Epoch 9/25

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[1m239/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5718 - loss: 1.0305
[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5722 - loss: 1.0310
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[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5732 - loss: 1.0310 - val_accuracy: 0.5348 - val_loss: 1.1137
Epoch 10/25

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[1m269/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5941 - loss: 1.0009
[1m299/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5945 - loss: 1.0000
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Epoch 11/25

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[1m179/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6203 - loss: 0.9356
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[1m238/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6203 - loss: 0.9353
[1m269/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6202 - loss: 0.9357
[1m297/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.6199 - loss: 0.9366
[1m326/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.6194 - loss: 0.9376
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.6193 - loss: 0.9378 - val_accuracy: 0.5548 - val_loss: 1.1113
Epoch 12/25

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5312 - loss: 0.9257
[1m 24/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6340 - loss: 0.9131 
[1m 53/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6337 - loss: 0.9171
[1m 84/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6316 - loss: 0.9181
[1m112/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6316 - loss: 0.9161
[1m142/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6323 - loss: 0.9136
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[1m207/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6336 - loss: 0.9105
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[1m265/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6337 - loss: 0.9095
[1m295/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6333 - loss: 0.9099
[1m323/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.6327 - loss: 0.9106
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.6326 - loss: 0.9107 - val_accuracy: 0.5411 - val_loss: 1.1396
Epoch 13/25

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[1m 32/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6799 - loss: 0.8281 
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Epoch 14/25

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Epoch 15/25

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Epoch 16/25

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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m20s[0m 422ms/step2025-11-08 10:17:57.943585: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_141', 8 bytes spill stores, 8 bytes spill loads

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Saved model to disk.
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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Loaded model from disk.
(1545, 6, 250)
(10469, 6, 250)

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6:55[0m 1s/step
[1m 57/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 902us/step
[1m118/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 865us/step
[1m179/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 854us/step
[1m236/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 861us/step
[1m298/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 851us/step
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[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 4ms/step

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

[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 17ms/step
[1m57/89[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 901us/step
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step  
Global accuracy score (validation) = 55.13 [%]
Global F1 score (validation) = 53.8 [%]
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[[0.0545352  0.87343115 0.06507601 0.00695765]
 [0.13673823 0.15333915 0.6916564  0.01826624]
 [0.37126002 0.18773696 0.37020314 0.07079992]
 ...
 [0.25612634 0.125582   0.43109906 0.1871927 ]
 [0.25893307 0.10627005 0.25868174 0.3761151 ]
 [0.07117873 0.02938434 0.03800811 0.86142886]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 70.76 [%]
Global accuracy score (test) = 51.39 [%]
Global F1 score (train) = 70.65 [%]
Global F1 score (test) = 51.22 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.42      0.40      0.41       400
MODERATE-INTENSITY       0.47      0.42      0.44       400
         SEDENTARY       0.59      0.66      0.62       400
VIGOROUS-INTENSITY       0.56      0.59      0.57       345

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


Accuracy capturado en la ejecución 18: 51.39 [%]
F1-score capturado en la ejecución 18: 51.22 [%]

=== EJECUCIÓN 19 ===

--- TRAIN (ejecución 19) ---

--- TEST (ejecución 19) ---
2025-11-08 10:18:08.941591: 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 10:18:08.952991: 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 00:00:1762593488.966483   93086 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762593488.970577   93086 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762593488.980409   93086 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762593488.980426   93086 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762593488.980427   93086 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762593488.980428   93086 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-08 10:18:08.983534: 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 00:00:1762593491.334776   93086 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 1/25
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762593493.866003   93204 service.cc:152] XLA service 0x7ac6b80065b0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762593493.866033   93204 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-08 10:18:13.918558: 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 00:00:1762593494.226602   93204 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762593496.367124   93204 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m23:38[0m 4s/step - accuracy: 0.3438 - loss: 1.6432
[1m 27/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2625 - loss: 1.9410  
[1m 57/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2891 - loss: 1.8522
[1m 87/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3042 - loss: 1.8043
[1m116/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3115 - loss: 1.7756
[1m144/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3175 - loss: 1.7517
[1m175/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3228 - loss: 1.7317
[1m207/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3282 - loss: 1.7128
[1m239/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3328 - loss: 1.6962
[1m268/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3361 - loss: 1.6829
[1m296/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.3388 - loss: 1.6716
[1m326/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.3413 - loss: 1.6611
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step - accuracy: 0.3414 - loss: 1.6604
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m8s[0m 12ms/step - accuracy: 0.3415 - loss: 1.6601 - val_accuracy: 0.4410 - val_loss: 1.2593
Epoch 2/25

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.3750 - loss: 1.2877
[1m 28/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4056 - loss: 1.3769 
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[1m 87/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3928 - loss: 1.3885
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Epoch 3/25

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Epoch 4/25

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Epoch 5/25

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[1m 27/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4083 - loss: 1.2515 
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[1m141/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4487 - loss: 1.2294
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Epoch 6/25

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Epoch 7/25

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Epoch 8/25

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Epoch 9/25

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[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5695 - loss: 1.0539 - val_accuracy: 0.5607 - val_loss: 1.0628
Epoch 10/25

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[1m 28/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5854 - loss: 1.0064 
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[1m119/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5909 - loss: 0.9993
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[1m245/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5898 - loss: 0.9978
[1m274/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5896 - loss: 0.9976
[1m304/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5894 - loss: 0.9975
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Epoch 11/25

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[1m 28/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5930 - loss: 0.9285 
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[1m178/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6038 - loss: 0.9595
[1m209/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6044 - loss: 0.9600
[1m238/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6052 - loss: 0.9601
[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6058 - loss: 0.9602
[1m301/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.6063 - loss: 0.9604
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.6066 - loss: 0.9607 - val_accuracy: 0.5558 - val_loss: 1.0786
Epoch 12/25

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.5000 - loss: 1.1263
[1m 31/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5982 - loss: 0.9671 
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[1m126/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6138 - loss: 0.9469
[1m156/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6144 - loss: 0.9462
[1m188/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6147 - loss: 0.9453
[1m217/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6149 - loss: 0.9448
[1m246/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6153 - loss: 0.9443
[1m274/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6158 - loss: 0.9435
[1m306/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.6164 - loss: 0.9425
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.6169 - loss: 0.9417 - val_accuracy: 0.5523 - val_loss: 1.1053
Epoch 13/25

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 23ms/step - accuracy: 0.6562 - loss: 0.7081
[1m 30/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6544 - loss: 0.8512 
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[1m 92/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6519 - loss: 0.8695
[1m124/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6509 - loss: 0.8730
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[1m187/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6497 - loss: 0.8769
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Epoch 14/25

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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m19s[0m 407ms/step2025-11-08 10:18:30.928688: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_141', 8 bytes spill stores, 8 bytes spill loads

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Saved model to disk.
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Loaded model from disk.
(1545, 6, 250)
(10469, 6, 250)

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6:46[0m 1s/step
[1m 55/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 933us/step
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[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m61/89[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 846us/step
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 979us/step
Global accuracy score (validation) = 54.63 [%]
Global F1 score (validation) = 53.55 [%]
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[[0.21355535 0.09825902 0.57952034 0.10866527]
 [0.07362851 0.8997406  0.02544199 0.00118897]
 [0.15910052 0.09738705 0.6632623  0.08025015]
 ...
 [0.15405001 0.03291292 0.19605482 0.6169823 ]
 [0.24926291 0.08864365 0.16845374 0.49363974]
 [0.45886648 0.19947356 0.19929509 0.1423648 ]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 70.01 [%]
Global accuracy score (test) = 50.87 [%]
Global F1 score (train) = 69.81 [%]
Global F1 score (test) = 50.44 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.38      0.34      0.36       400
MODERATE-INTENSITY       0.44      0.40      0.42       400
         SEDENTARY       0.62      0.72      0.67       400
VIGOROUS-INTENSITY       0.55      0.59      0.57       345

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


Accuracy capturado en la ejecución 19: 50.87 [%]
F1-score capturado en la ejecución 19: 50.44 [%]

=== EJECUCIÓN 20 ===

--- TRAIN (ejecución 20) ---

--- TEST (ejecución 20) ---
2025-11-08 10:18:41.856823: 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 10:18:41.868588: 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 00:00:1762593521.881971   95395 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762593521.885935   95395 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762593521.896283   95395 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762593521.896300   95395 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762593521.896302   95395 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762593521.896303   95395 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-08 10:18:41.899384: 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 00:00:1762593524.243448   95395 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 1/25
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762593526.724228   95524 service.cc:152] XLA service 0x7a7818015130 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762593526.724274   95524 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-08 10:18:46.776142: 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 00:00:1762593527.083236   95524 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762593529.240297   95524 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 2/25

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Epoch 3/25

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[1m284/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4473 - loss: 1.2656
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Epoch 4/25

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Epoch 5/25

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Epoch 6/25

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[1m302/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5105 - loss: 1.1445
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Epoch 7/25

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[1m 31/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5701 - loss: 1.0861 
[1m 60/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5701 - loss: 1.0823
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[1m118/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5598 - loss: 1.0866
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[1m209/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5523 - loss: 1.0891
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[1m271/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5503 - loss: 1.0902
[1m302/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5496 - loss: 1.0905
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Epoch 8/25

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[1m 32/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5538 - loss: 1.0784 
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Epoch 9/25

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Epoch 10/25

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[1m113/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6382 - loss: 0.9228
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[1m289/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6286 - loss: 0.9357
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[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.6266 - loss: 0.9381 - val_accuracy: 0.5460 - val_loss: 1.0946
Epoch 11/25

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[1m 27/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6383 - loss: 0.9068 
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[1m113/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6322 - loss: 0.9116
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[1m174/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6316 - loss: 0.9150
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[1m292/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6312 - loss: 0.9164
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Epoch 12/25

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[1m 27/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6672 - loss: 0.8519 
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[1m260/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6557 - loss: 0.8721
[1m291/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6549 - loss: 0.8740
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Epoch 13/25

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.6875 - loss: 0.8685
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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m20s[0m 424ms/step2025-11-08 10:19:03.124483: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_141', 8 bytes spill stores, 8 bytes spill loads


[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 21ms/step  
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Saved model to disk.
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Loaded model from disk.
(1545, 6, 250)
(10469, 6, 250)

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6:47[0m 1s/step
[1m 52/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 982us/step
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[1m47/49[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step 
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 18ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 18ms/step

[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m60/89[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 855us/step
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 986us/step
Global accuracy score (validation) = 54.71 [%]
Global F1 score (validation) = 54.49 [%]
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[[0.24241765 0.13667955 0.5472192  0.07368356]
 [0.19087079 0.16639355 0.6164049  0.02633087]
 [0.23492907 0.14525859 0.5618908  0.05792159]
 ...
 [0.05539611 0.01675691 0.01642504 0.911422  ]
 [0.28510657 0.10938723 0.3141545  0.29135168]
 [0.04690655 0.01399808 0.01342091 0.92567444]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 70.21 [%]
Global accuracy score (test) = 53.33 [%]
Global F1 score (train) = 70.59 [%]
Global F1 score (test) = 54.09 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.38      0.40      0.39       400
MODERATE-INTENSITY       0.49      0.57      0.53       400
         SEDENTARY       0.60      0.62      0.61       400
VIGOROUS-INTENSITY       0.76      0.55      0.64       345

          accuracy                           0.53      1545
         macro avg       0.56      0.53      0.54      1545
      weighted avg       0.55      0.53      0.54      1545


Accuracy capturado en la ejecución 20: 53.33 [%]
F1-score capturado en la ejecución 20: 54.09 [%]

=== EJECUCIÓN 21 ===

--- TRAIN (ejecución 21) ---

--- TEST (ejecución 21) ---
2025-11-08 10:19:14.233938: 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 10:19:14.245355: 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 00:00:1762593554.258491   97608 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762593554.262602   97608 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762593554.272804   97608 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762593554.272822   97608 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762593554.272823   97608 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762593554.272824   97608 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-08 10:19:14.275758: 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 00:00:1762593556.619615   97608 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 1/25
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762593559.189668   97736 service.cc:152] XLA service 0x7f0040006f00 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762593559.189722   97736 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-08 10:19:19.244498: 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 00:00:1762593559.566124   97736 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762593561.735638   97736 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 2/25

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[1m120/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3769 - loss: 1.4431
[1m153/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3788 - loss: 1.4384
[1m183/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3808 - loss: 1.4336
[1m212/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3829 - loss: 1.4298
[1m236/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3843 - loss: 1.4272
[1m263/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3859 - loss: 1.4243
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Epoch 3/25

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Epoch 4/25

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Epoch 5/25

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Epoch 6/25

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Epoch 7/25

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Epoch 8/25

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[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5449 - loss: 1.0891
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Epoch 9/25

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[1m115/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5576 - loss: 1.0772
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[1m293/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5652 - loss: 1.0557
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[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5658 - loss: 1.0534 - val_accuracy: 0.5418 - val_loss: 1.1315
Epoch 10/25

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[1m 26/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6039 - loss: 0.9646 
[1m 57/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6003 - loss: 0.9684
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[1m113/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5962 - loss: 0.9753
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[1m202/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5910 - loss: 0.9831
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[1m291/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5899 - loss: 0.9866
[1m322/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.5897 - loss: 0.9875
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5897 - loss: 0.9876 - val_accuracy: 0.5509 - val_loss: 1.1061
Epoch 11/25

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[1m 28/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6072 - loss: 0.9590 
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Epoch 12/25

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[1m268/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6245 - loss: 0.9366
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Epoch 13/25

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[1m 27/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5890 - loss: 0.9151 
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[1m235/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6345 - loss: 0.8890
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[1m292/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6359 - loss: 0.8885
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[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.6366 - loss: 0.8882 - val_accuracy: 0.5390 - val_loss: 1.1747
Epoch 14/25

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[1m 30/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6605 - loss: 0.8730 
[1m 60/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6540 - loss: 0.8770
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[1m119/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6532 - loss: 0.8741
[1m147/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6534 - loss: 0.8724
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[1m203/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6535 - loss: 0.8692
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[1m258/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6542 - loss: 0.8668
[1m288/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6548 - loss: 0.8656
[1m319/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.6554 - loss: 0.8642
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.6556 - loss: 0.8637 - val_accuracy: 0.5562 - val_loss: 1.1493
Epoch 15/25

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[1m 29/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6714 - loss: 0.8478 
[1m 58/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6739 - loss: 0.8299
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Epoch 16/25

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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m20s[0m 419ms/step2025-11-08 10:19:37.926245: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_141', 8 bytes spill stores, 8 bytes spill loads

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Saved model to disk.
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Loaded model from disk.
(1545, 6, 250)
(10469, 6, 250)

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[1m 52/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 985us/step
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[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m57/89[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 894us/step
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Global accuracy score (validation) = 55.48 [%]
Global F1 score (validation) = 54.69 [%]
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[[0.3480366  0.41550127 0.15150382 0.08495837]
 [0.23074614 0.18667272 0.4458817  0.13669945]
 [0.21982093 0.42992827 0.27706626 0.07318459]
 ...
 [0.22894838 0.1220815  0.34068578 0.30828437]
 [0.20997955 0.20960839 0.4216236  0.15878846]
 [0.22705498 0.19112396 0.39354962 0.18827143]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 68.52 [%]
Global accuracy score (test) = 52.62 [%]
Global F1 score (train) = 68.77 [%]
Global F1 score (test) = 52.43 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.39      0.35      0.37       400
MODERATE-INTENSITY       0.44      0.45      0.44       400
         SEDENTARY       0.59      0.78      0.67       400
VIGOROUS-INTENSITY       0.74      0.52      0.61       345

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


Accuracy capturado en la ejecución 21: 52.62 [%]
F1-score capturado en la ejecución 21: 52.43 [%]

=== EJECUCIÓN 22 ===

--- TRAIN (ejecución 22) ---

--- TEST (ejecución 22) ---
2025-11-08 10:19:48.819111: 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 10:19:48.830189: 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 00:00:1762593588.843297  100143 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762593588.847443  100143 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762593588.857153  100143 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762593588.857170  100143 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762593588.857171  100143 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762593588.857173  100143 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-08 10:19:48.860295: 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 00:00:1762593591.220531  100143 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 1/25
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762593593.694024  100273 service.cc:152] XLA service 0x74e61c014820 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762593593.694069  100273 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-08 10:19:53.746316: 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 00:00:1762593594.062088  100273 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762593596.225332  100273 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 54/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3017 - loss: 1.7212
[1m 81/328[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3104 - loss: 1.7086
[1m111/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3155 - loss: 1.7004
[1m143/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3196 - loss: 1.6922
[1m172/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3230 - loss: 1.6849
[1m205/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3263 - loss: 1.6764
[1m236/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3295 - loss: 1.6675
[1m265/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3319 - loss: 1.6597
[1m294/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3340 - loss: 1.6527
[1m325/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.3359 - loss: 1.6456
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step - accuracy: 0.3360 - loss: 1.6450
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m8s[0m 12ms/step - accuracy: 0.3361 - loss: 1.6447 - val_accuracy: 0.4235 - val_loss: 1.2757
Epoch 2/25

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2812 - loss: 1.6048
[1m 29/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3841 - loss: 1.4480 
[1m 58/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3903 - loss: 1.4419
[1m 87/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3876 - loss: 1.4476
[1m115/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3869 - loss: 1.4468
[1m144/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3885 - loss: 1.4419
[1m174/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3905 - loss: 1.4363
[1m203/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3922 - loss: 1.4312
[1m233/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3936 - loss: 1.4266
[1m263/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3948 - loss: 1.4224
[1m292/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3960 - loss: 1.4188
[1m321/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.3972 - loss: 1.4151
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3975 - loss: 1.4142 - val_accuracy: 0.4614 - val_loss: 1.2273
Epoch 3/25

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 23ms/step - accuracy: 0.5938 - loss: 0.9494
[1m 28/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4480 - loss: 1.2684 
[1m 56/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4391 - loss: 1.2922
[1m 86/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4376 - loss: 1.2964
[1m112/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4366 - loss: 1.2983
[1m142/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4351 - loss: 1.2999
[1m171/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4342 - loss: 1.3010
[1m201/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4333 - loss: 1.3015
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[1m264/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4323 - loss: 1.3019
[1m295/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4323 - loss: 1.3013
[1m325/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.4325 - loss: 1.3002
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Epoch 4/25

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Epoch 5/25

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Epoch 6/25

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Epoch 7/25

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Epoch 8/25

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[1m261/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5541 - loss: 1.0742
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Epoch 9/25

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[1m117/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5827 - loss: 1.0161
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[1m266/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5794 - loss: 1.0174
[1m293/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5792 - loss: 1.0177
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[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5792 - loss: 1.0176 - val_accuracy: 0.5421 - val_loss: 1.0879
Epoch 10/25

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[1m 25/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5291 - loss: 1.0470 
[1m 55/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5492 - loss: 1.0264
[1m 83/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5605 - loss: 1.0146
[1m112/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5682 - loss: 1.0074
[1m141/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5736 - loss: 1.0011
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[1m203/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5811 - loss: 0.9923
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[1m295/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5866 - loss: 0.9850
[1m326/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.5876 - loss: 0.9839
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5877 - loss: 0.9838 - val_accuracy: 0.5362 - val_loss: 1.1040
Epoch 11/25

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[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6202 - loss: 0.9410
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Epoch 12/25

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Epoch 13/25

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Epoch 14/25

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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m19s[0m 415ms/step2025-11-08 10:20:10.860547: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_141', 8 bytes spill stores, 8 bytes spill loads

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Saved model to disk.
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
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Loaded model from disk.
(1545, 6, 250)
(10469, 6, 250)

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6:48[0m 1s/step
[1m 59/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 873us/step
[1m120/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 850us/step
[1m176/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 864us/step
[1m231/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 878us/step
[1m291/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 871us/step
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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 16ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 18ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 19ms/step

[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m56/89[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 923us/step
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step  
Global accuracy score (validation) = 55.3 [%]
Global F1 score (validation) = 54.61 [%]
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[[0.18831602 0.14874348 0.6315953  0.03134513]
 [0.24670453 0.13762715 0.526045   0.0896233 ]
 [0.18628034 0.14776523 0.6357939  0.03016046]
 ...
 [0.334064   0.11082771 0.27761093 0.27749732]
 [0.18019556 0.03429826 0.08875275 0.6967534 ]
 [0.26284957 0.04876588 0.10160101 0.5867835 ]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 70.9 [%]
Global accuracy score (test) = 52.88 [%]
Global F1 score (train) = 71.16 [%]
Global F1 score (test) = 53.58 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.37      0.39      0.38       400
MODERATE-INTENSITY       0.45      0.46      0.45       400
         SEDENTARY       0.64      0.66      0.65       400
VIGOROUS-INTENSITY       0.71      0.62      0.66       345

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


Accuracy capturado en la ejecución 22: 52.88 [%]
F1-score capturado en la ejecución 22: 53.58 [%]

=== EJECUCIÓN 23 ===

--- TRAIN (ejecución 23) ---

--- TEST (ejecución 23) ---
2025-11-08 10:20:21.808362: 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 10:20:21.819611: 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 00:00:1762593621.832615  102469 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762593621.836661  102469 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762593621.846347  102469 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762593621.846365  102469 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762593621.846366  102469 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762593621.846367  102469 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-08 10:20:21.849447: 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 00:00:1762593624.190464  102469 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 1/25
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762593626.699706  102587 service.cc:152] XLA service 0x7d4b68017a00 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762593626.699739  102587 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-08 10:20:26.749882: 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 00:00:1762593627.071277  102587 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762593629.251665  102587 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m23:50[0m 4s/step - accuracy: 0.1875 - loss: 2.4215
[1m 26/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2356 - loss: 2.0090  
[1m 57/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2615 - loss: 1.8989
[1m 85/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2785 - loss: 1.8523
[1m118/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2908 - loss: 1.8183
[1m149/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2992 - loss: 1.7921
[1m179/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3050 - loss: 1.7718
[1m205/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3090 - loss: 1.7571
[1m235/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3127 - loss: 1.7431
[1m264/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3157 - loss: 1.7311
[1m296/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.3187 - loss: 1.7192
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Epoch 2/25

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Epoch 3/25

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[1m181/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4032 - loss: 1.3550
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[1m238/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4055 - loss: 1.3509
[1m266/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4066 - loss: 1.3487
[1m298/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4077 - loss: 1.3465
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Epoch 4/25

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[1m149/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4532 - loss: 1.2676
[1m182/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4521 - loss: 1.2674
[1m212/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4512 - loss: 1.2677
[1m242/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4504 - loss: 1.2679
[1m271/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4499 - loss: 1.2679
[1m303/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4496 - loss: 1.2676
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4495 - loss: 1.2674 - val_accuracy: 0.5067 - val_loss: 1.1589
Epoch 5/25

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[1m 32/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4491 - loss: 1.2881 
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[1m 93/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4527 - loss: 1.2750
[1m127/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4543 - loss: 1.2681
[1m156/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4555 - loss: 1.2637
[1m186/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4567 - loss: 1.2595
[1m218/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4579 - loss: 1.2560
[1m248/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4589 - loss: 1.2534
[1m276/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4600 - loss: 1.2509
[1m306/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4612 - loss: 1.2483
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4622 - loss: 1.2465 - val_accuracy: 0.5186 - val_loss: 1.1531
Epoch 6/25

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[1m 31/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4785 - loss: 1.2156 
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[1m 89/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4832 - loss: 1.2111
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[1m297/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4904 - loss: 1.1965
[1m327/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.4910 - loss: 1.1954
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Epoch 7/25

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[1m268/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5191 - loss: 1.1491
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Epoch 8/25

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[1m114/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5519 - loss: 1.0977
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[1m175/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5504 - loss: 1.1011
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[1m232/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5487 - loss: 1.1031
[1m264/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5479 - loss: 1.1033
[1m291/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5473 - loss: 1.1034
[1m321/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.5469 - loss: 1.1035
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5468 - loss: 1.1035 - val_accuracy: 0.5456 - val_loss: 1.1136
Epoch 9/25

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[1m 27/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5926 - loss: 1.0337 
[1m 58/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5724 - loss: 1.0610
[1m 87/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5678 - loss: 1.0652
[1m115/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5678 - loss: 1.0632
[1m144/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5686 - loss: 1.0606
[1m173/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5691 - loss: 1.0580
[1m203/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5695 - loss: 1.0561
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[1m258/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5703 - loss: 1.0541
[1m289/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5702 - loss: 1.0538
[1m321/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.5700 - loss: 1.0537
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5699 - loss: 1.0538 - val_accuracy: 0.5323 - val_loss: 1.1571
Epoch 10/25

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[1m 30/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5770 - loss: 1.0172 
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[1m244/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5887 - loss: 1.0140
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Epoch 11/25

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[1m271/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6098 - loss: 0.9640
[1m297/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.6100 - loss: 0.9644
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Epoch 12/25

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[1m121/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6140 - loss: 0.9578
[1m152/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6168 - loss: 0.9534
[1m182/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6191 - loss: 0.9498
[1m212/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6210 - loss: 0.9467
[1m241/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6223 - loss: 0.9449
[1m271/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6233 - loss: 0.9436
[1m300/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.6240 - loss: 0.9427
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.6244 - loss: 0.9422 - val_accuracy: 0.5646 - val_loss: 1.1409
Epoch 13/25

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[1m 32/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6606 - loss: 0.8963 
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[1m 90/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6672 - loss: 0.8799
[1m122/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6663 - loss: 0.8772
[1m154/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6647 - loss: 0.8777
[1m186/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6636 - loss: 0.8785
[1m216/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6627 - loss: 0.8793
[1m247/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6619 - loss: 0.8799
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[1m305/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.6606 - loss: 0.8809
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Epoch 14/25

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[1m 28/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6962 - loss: 0.8248 
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Epoch 15/25

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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m19s[0m 415ms/step2025-11-08 10:20:44.507910: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_141', 8 bytes spill stores, 8 bytes spill loads

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Saved model to disk.
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Loaded model from disk.
(1545, 6, 250)
(10469, 6, 250)

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[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m53/89[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 976us/step
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Global accuracy score (validation) = 55.13 [%]
Global F1 score (validation) = 53.74 [%]
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[[0.16873898 0.10678314 0.66212076 0.06235709]
 [0.12249137 0.10037383 0.7531377  0.02399714]
 [0.11886958 0.09857445 0.75988215 0.02267382]
 ...
 [0.20192353 0.08869902 0.17950583 0.52987164]
 [0.22459227 0.11242031 0.5234483  0.13953921]
 [0.01893427 0.00597496 0.0078373  0.96725357]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 69.35 [%]
Global accuracy score (test) = 52.36 [%]
Global F1 score (train) = 69.17 [%]
Global F1 score (test) = 52.02 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.41      0.35      0.38       400
MODERATE-INTENSITY       0.46      0.48      0.47       400
         SEDENTARY       0.61      0.72      0.66       400
VIGOROUS-INTENSITY       0.59      0.56      0.57       345

          accuracy                           0.52      1545
         macro avg       0.52      0.52      0.52      1545
      weighted avg       0.52      0.52      0.52      1545


Accuracy capturado en la ejecución 23: 52.36 [%]
F1-score capturado en la ejecución 23: 52.02 [%]

=== EJECUCIÓN 24 ===

--- TRAIN (ejecución 24) ---

--- TEST (ejecución 24) ---
2025-11-08 10:20:55.510589: 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 10:20:55.521975: 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 00:00:1762593655.535275  104870 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762593655.539283  104870 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762593655.549351  104870 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762593655.549371  104870 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762593655.549373  104870 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762593655.549374  104870 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-08 10:20:55.552586: 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 00:00:1762593657.921365  104870 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 1/25
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762593660.430155  104993 service.cc:152] XLA service 0x7b1268015a30 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762593660.430200  104993 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-08 10:21:00.482191: 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 00:00:1762593660.788476  104993 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762593662.944557  104993 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 51/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2539 - loss: 1.8037
[1m 84/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2747 - loss: 1.7648
[1m113/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2867 - loss: 1.7394
[1m142/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2950 - loss: 1.7198
[1m172/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3020 - loss: 1.7021
[1m201/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3073 - loss: 1.6874
[1m228/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3116 - loss: 1.6748
[1m259/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3158 - loss: 1.6616
[1m291/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3194 - loss: 1.6496
[1m322/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.3226 - loss: 1.6388
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step - accuracy: 0.3231 - loss: 1.6369
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m8s[0m 12ms/step - accuracy: 0.3232 - loss: 1.6366 - val_accuracy: 0.4438 - val_loss: 1.2721
Epoch 2/25

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.6250 - loss: 1.1779
[1m 30/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4249 - loss: 1.3395 
[1m 59/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4106 - loss: 1.3528
[1m 89/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4059 - loss: 1.3595
[1m117/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4047 - loss: 1.3616
[1m147/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4041 - loss: 1.3642
[1m178/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4034 - loss: 1.3658
[1m211/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4030 - loss: 1.3656
[1m241/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4032 - loss: 1.3647
[1m272/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4038 - loss: 1.3632
[1m302/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4045 - loss: 1.3619
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4049 - loss: 1.3611 - val_accuracy: 0.4529 - val_loss: 1.2151
Epoch 3/25

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.3438 - loss: 1.3178
[1m 31/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3957 - loss: 1.2919 
[1m 61/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4069 - loss: 1.2879
[1m 89/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4119 - loss: 1.2890
[1m118/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4159 - loss: 1.2877
[1m147/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4188 - loss: 1.2856
[1m177/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4206 - loss: 1.2847
[1m208/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4225 - loss: 1.2835
[1m240/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4241 - loss: 1.2823
[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4256 - loss: 1.2813
[1m299/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4269 - loss: 1.2803
[1m327/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.4280 - loss: 1.2792
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4281 - loss: 1.2791 - val_accuracy: 0.4870 - val_loss: 1.1844
Epoch 4/25

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Epoch 5/25

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Epoch 6/25

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[1m291/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5055 - loss: 1.1428
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Epoch 7/25

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Epoch 8/25

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Epoch 9/25

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Epoch 10/25

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[1m290/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6015 - loss: 0.9771
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Epoch 11/25

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Epoch 12/25

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Epoch 13/25

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Epoch 14/25

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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m19s[0m 413ms/step2025-11-08 10:21:17.520783: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_141', 8 bytes spill stores, 8 bytes spill loads

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Saved model to disk.
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Loaded model from disk.
(1545, 6, 250)
(10469, 6, 250)

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6:46[0m 1s/step
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[1m170/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 892us/step
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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 17ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 18ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 19ms/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) = 54.85 [%]
Global F1 score (validation) = 53.6 [%]
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[[0.24704044 0.15384477 0.5068101  0.09230462]
 [0.28094268 0.25488272 0.324587   0.13958761]
 [0.1353891  0.09262048 0.74226195 0.02972846]
 ...
 [0.03957516 0.01899214 0.03876727 0.90266544]
 [0.17305398 0.08180168 0.24567768 0.49946663]
 [0.03890587 0.01581951 0.02988124 0.91539335]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 69.02 [%]
Global accuracy score (test) = 56.12 [%]
Global F1 score (train) = 68.95 [%]
Global F1 score (test) = 55.38 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.47      0.30      0.37       400
MODERATE-INTENSITY       0.48      0.57      0.52       400
         SEDENTARY       0.62      0.76      0.68       400
VIGOROUS-INTENSITY       0.68      0.61      0.64       345

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


Accuracy capturado en la ejecución 24: 56.12 [%]
F1-score capturado en la ejecución 24: 55.38 [%]

=== EJECUCIÓN 25 ===

--- TRAIN (ejecución 25) ---

--- TEST (ejecución 25) ---
2025-11-08 10:21:28.633711: 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 10:21:28.644942: 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 00:00:1762593688.658109  107197 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762593688.662115  107197 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762593688.672011  107197 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762593688.672027  107197 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762593688.672029  107197 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762593688.672030  107197 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-08 10:21:28.675001: 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 00:00:1762593691.052731  107197 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 1/25
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762593693.573615  107325 service.cc:152] XLA service 0x7c8a08014ee0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762593693.573644  107325 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-08 10:21:33.627967: 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 00:00:1762593693.949235  107325 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762593696.124595  107325 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m23:58[0m 4s/step - accuracy: 0.4062 - loss: 1.5858
[1m 24/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3247 - loss: 1.6287  
[1m 55/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3272 - loss: 1.6322
[1m 83/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3303 - loss: 1.6339
[1m115/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3345 - loss: 1.6284
[1m147/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3379 - loss: 1.6209
[1m178/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3410 - loss: 1.6131
[1m205/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3433 - loss: 1.6066
[1m235/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3453 - loss: 1.5997
[1m265/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3472 - loss: 1.5931
[1m294/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3487 - loss: 1.5870
[1m323/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.3501 - loss: 1.5809
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step - accuracy: 0.3504 - loss: 1.5799
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Epoch 2/25

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[1m 59/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4037 - loss: 1.3725
[1m 90/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4079 - loss: 1.3635
[1m119/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4109 - loss: 1.3581
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[1m178/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4139 - loss: 1.3521
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Epoch 3/25

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Epoch 4/25

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[1m291/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4697 - loss: 1.2112
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[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4696 - loss: 1.2114 - val_accuracy: 0.5246 - val_loss: 1.1357
Epoch 5/25

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[1m 28/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4519 - loss: 1.1866 
[1m 60/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4729 - loss: 1.1755
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[1m120/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4854 - loss: 1.1748
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[1m234/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4883 - loss: 1.1779
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Epoch 6/25

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[1m299/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5195 - loss: 1.1319
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Epoch 7/25

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[1m293/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5432 - loss: 1.0759
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Epoch 8/25

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[1m119/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5693 - loss: 1.0389
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[1m177/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5668 - loss: 1.0410
[1m208/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5663 - loss: 1.0412
[1m239/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5656 - loss: 1.0416
[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5655 - loss: 1.0413
[1m297/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5657 - loss: 1.0407
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5660 - loss: 1.0398 - val_accuracy: 0.5471 - val_loss: 1.1039
Epoch 9/25

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[1m 27/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6017 - loss: 0.9647 
[1m 57/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5945 - loss: 0.9840
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[1m114/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5879 - loss: 0.9930
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Epoch 10/25

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Epoch 11/25

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Epoch 12/25

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[1m292/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6326 - loss: 0.8877
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Epoch 13/25

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Epoch 14/25

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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m19s[0m 416ms/step2025-11-08 10:21:50.808071: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_141', 8 bytes spill stores, 8 bytes spill loads

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Saved model to disk.
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Loaded model from disk.
(1545, 6, 250)
(10469, 6, 250)

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6:50[0m 1s/step
[1m 53/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 987us/step
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[1m163/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 945us/step
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[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 18ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 18ms/step

[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/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) = 55.9 [%]
Global F1 score (validation) = 55.08 [%]
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[[0.17857029 0.07458934 0.5647269  0.1821135 ]
 [0.17608246 0.06257346 0.44107664 0.32026747]
 [0.48124576 0.36476886 0.14428832 0.00969704]
 ...
 [0.03039341 0.00797553 0.01780359 0.9438275 ]
 [0.07853073 0.04042502 0.27899176 0.6020525 ]
 [0.06246551 0.0262853  0.03014343 0.8811058 ]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 65.23 [%]
Global accuracy score (test) = 50.87 [%]
Global F1 score (train) = 64.77 [%]
Global F1 score (test) = 51.15 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.38      0.36      0.37       400
MODERATE-INTENSITY       0.46      0.46      0.46       400
         SEDENTARY       0.72      0.60      0.65       400
VIGOROUS-INTENSITY       0.51      0.63      0.57       345

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


Accuracy capturado en la ejecución 25: 50.87 [%]
F1-score capturado en la ejecución 25: 51.15 [%]

=== EJECUCIÓN 26 ===

--- TRAIN (ejecución 26) ---

--- TEST (ejecución 26) ---
2025-11-08 10:22:01.910532: 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 10:22:01.921860: 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 00:00:1762593721.935101  109528 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762593721.939163  109528 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762593721.948884  109528 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762593721.948901  109528 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762593721.948902  109528 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762593721.948903  109528 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-08 10:22:01.951978: 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 00:00:1762593724.260964  109528 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 1/25
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762593726.744949  109641 service.cc:152] XLA service 0x7610780134a0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762593726.744976  109641 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-08 10:22:06.797606: 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 00:00:1762593727.106184  109641 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762593729.231522  109641 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 2/25

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Epoch 3/25

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Epoch 4/25

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[1m309/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4581 - loss: 1.2567
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Epoch 5/25

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[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4759 - loss: 1.2076
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Epoch 6/25

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[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5070 - loss: 1.1470 - val_accuracy: 0.5284 - val_loss: 1.1071
Epoch 7/25

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[1m 32/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5366 - loss: 1.1168 
[1m 63/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5382 - loss: 1.1097
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[1m124/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5377 - loss: 1.1058
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[1m279/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5371 - loss: 1.1020
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Epoch 8/25

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4375 - loss: 1.2292
[1m 28/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5579 - loss: 1.0574 
[1m 57/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5531 - loss: 1.0568
[1m 89/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5522 - loss: 1.0588
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[1m297/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5553 - loss: 1.0565
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Epoch 9/25

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[1m 29/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5522 - loss: 1.1081 
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[1m234/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5684 - loss: 1.0430
[1m261/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5689 - loss: 1.0409
[1m291/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5693 - loss: 1.0389
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Epoch 10/25

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[1m126/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5922 - loss: 0.9800
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[1m184/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5918 - loss: 0.9819
[1m217/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5916 - loss: 0.9824
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[1m276/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5911 - loss: 0.9830
[1m304/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5911 - loss: 0.9831
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5912 - loss: 0.9832 - val_accuracy: 0.5435 - val_loss: 1.0929
Epoch 11/25

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[1m 27/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6102 - loss: 0.9664 
[1m 58/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6015 - loss: 0.9670
[1m 86/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6004 - loss: 0.9640
[1m116/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5999 - loss: 0.9623
[1m145/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6005 - loss: 0.9602
[1m174/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6008 - loss: 0.9597
[1m203/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6012 - loss: 0.9587
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[1m290/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6018 - loss: 0.9564
[1m318/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.6023 - loss: 0.9556
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.6025 - loss: 0.9553 - val_accuracy: 0.5555 - val_loss: 1.1118
Epoch 12/25

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.6562 - loss: 1.0488
[1m 32/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6189 - loss: 0.9414 
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Epoch 13/25

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Epoch 14/25

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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m19s[0m 414ms/step2025-11-08 10:22:23.813004: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_141', 8 bytes spill stores, 8 bytes spill loads

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[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 21ms/step
Saved model to disk.
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
Loaded model from disk.
(1545, 6, 250)
(10469, 6, 250)

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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 17ms/step
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[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 18ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 19ms/step

[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m60/89[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 863us/step
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 978us/step
Global accuracy score (validation) = 55.9 [%]
Global F1 score (validation) = 54.25 [%]
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[[0.13799524 0.15282068 0.66085905 0.04832506]
 [0.19834264 0.24085295 0.5163643  0.04444019]
 [0.13957289 0.15343124 0.6599031  0.04709267]
 ...
 [0.25969908 0.20336075 0.41182628 0.12511396]
 [0.12947783 0.06177786 0.05664643 0.7520979 ]
 [0.01096097 0.00398069 0.00357589 0.9814825 ]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 69.83 [%]
Global accuracy score (test) = 50.55 [%]
Global F1 score (train) = 69.24 [%]
Global F1 score (test) = 49.59 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.38      0.23      0.29       400
MODERATE-INTENSITY       0.44      0.58      0.50       400
         SEDENTARY       0.61      0.64      0.62       400
VIGOROUS-INTENSITY       0.56      0.59      0.57       345

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


Accuracy capturado en la ejecución 26: 50.55 [%]
F1-score capturado en la ejecución 26: 49.59 [%]

=== EJECUCIÓN 27 ===

--- TRAIN (ejecución 27) ---

--- TEST (ejecución 27) ---
2025-11-08 10:22:34.762910: 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 10:22:34.774099: 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 00:00:1762593754.787347  111829 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762593754.791546  111829 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762593754.801499  111829 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762593754.801525  111829 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762593754.801527  111829 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762593754.801528  111829 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-08 10:22:34.804663: 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 00:00:1762593757.140043  111829 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 1/25
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762593759.651949  111968 service.cc:152] XLA service 0x7202dc016220 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762593759.651993  111968 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-08 10:22:39.711259: 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 00:00:1762593760.039233  111968 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762593762.165782  111968 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m23:46[0m 4s/step - accuracy: 0.3438 - loss: 1.9542
[1m 28/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2956 - loss: 1.8340  
[1m 56/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2991 - loss: 1.8115
[1m 86/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3062 - loss: 1.7880
[1m117/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3141 - loss: 1.7639
[1m147/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3199 - loss: 1.7448
[1m176/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3246 - loss: 1.7274
[1m207/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3290 - loss: 1.7107
[1m237/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3323 - loss: 1.6976
[1m266/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3353 - loss: 1.6865
[1m297/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.3378 - loss: 1.6760
[1m325/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.3399 - loss: 1.6675
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step - accuracy: 0.3401 - loss: 1.6666
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m8s[0m 12ms/step - accuracy: 0.3402 - loss: 1.6663 - val_accuracy: 0.4716 - val_loss: 1.2184
Epoch 2/25

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4062 - loss: 1.2660
[1m 31/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4027 - loss: 1.3603 
[1m 57/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4028 - loss: 1.3699
[1m 87/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4015 - loss: 1.3804
[1m111/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4019 - loss: 1.3839
[1m141/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4023 - loss: 1.3850
[1m170/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4024 - loss: 1.3857
[1m201/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4024 - loss: 1.3861
[1m232/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4025 - loss: 1.3857
[1m262/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4026 - loss: 1.3848
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[1m324/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.4037 - loss: 1.3820
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Epoch 3/25

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Epoch 4/25

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Epoch 5/25

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Epoch 6/25

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[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5260 - loss: 1.1400 - val_accuracy: 0.5478 - val_loss: 1.0879
Epoch 7/25

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Epoch 8/25

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[1m114/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5652 - loss: 1.0540
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[1m290/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5628 - loss: 1.0549
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[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5630 - loss: 1.0538 - val_accuracy: 0.5499 - val_loss: 1.0768
Epoch 9/25

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[1m 31/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5939 - loss: 1.0120 
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[1m117/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5838 - loss: 1.0141
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[1m235/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5840 - loss: 1.0124
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[1m297/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5848 - loss: 1.0108
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[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5851 - loss: 1.0102 - val_accuracy: 0.5527 - val_loss: 1.0823
Epoch 10/25

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Epoch 11/25

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.6562 - loss: 0.9370
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Epoch 12/25

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[1m298/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.6529 - loss: 0.8860
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Epoch 13/25

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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m19s[0m 399ms/step2025-11-08 10:22:56.098422: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_141', 8 bytes spill stores, 8 bytes spill loads

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Saved model to disk.
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
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Loaded model from disk.
(1545, 6, 250)
(10469, 6, 250)

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6:37[0m 1s/step
[1m 51/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step 
[1m105/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 968us/step
[1m166/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 917us/step
[1m226/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 898us/step
[1m287/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 883us/step
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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 17ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 18ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 18ms/step

[1m 1/89[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) = 55.09 [%]
Global F1 score (validation) = 54.01 [%]
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[[0.19035223 0.14825587 0.56457937 0.09681258]
 [0.16095348 0.15126283 0.6383158  0.04946791]
 [0.14046484 0.1574807  0.69463503 0.00741944]
 ...
 [0.28910115 0.10656666 0.20077716 0.40355495]
 [0.25924185 0.1584505  0.22764638 0.35466126]
 [0.07330393 0.01870001 0.02218772 0.8858084 ]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 68.96 [%]
Global accuracy score (test) = 52.3 [%]
Global F1 score (train) = 68.49 [%]
Global F1 score (test) = 51.49 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.42      0.28      0.34       400
MODERATE-INTENSITY       0.46      0.54      0.50       400
         SEDENTARY       0.58      0.72      0.64       400
VIGOROUS-INTENSITY       0.62      0.55      0.59       345

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


Accuracy capturado en la ejecución 27: 52.3 [%]
F1-score capturado en la ejecución 27: 51.49 [%]

=== EJECUCIÓN 28 ===

--- TRAIN (ejecución 28) ---

--- TEST (ejecución 28) ---
2025-11-08 10:23:07.019810: 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 10:23:07.031259: 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 00:00:1762593787.044706  114048 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762593787.048795  114048 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762593787.058625  114048 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762593787.058644  114048 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762593787.058646  114048 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762593787.058654  114048 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-08 10:23:07.061795: 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 00:00:1762593789.421916  114048 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 1/25
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762593791.973106  114177 service.cc:152] XLA service 0x7450ac002710 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762593791.973132  114177 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-08 10:23:12.023455: 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 00:00:1762593792.347784  114177 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762593794.476890  114177 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m23:45[0m 4s/step - accuracy: 0.1562 - loss: 2.7473
[1m 29/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2408 - loss: 2.0898  
[1m 62/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2636 - loss: 1.9674
[1m 88/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2769 - loss: 1.9155
[1m117/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2878 - loss: 1.8756
[1m148/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2974 - loss: 1.8414
[1m177/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3051 - loss: 1.8146
[1m207/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3115 - loss: 1.7909
[1m237/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3167 - loss: 1.7708
[1m267/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3206 - loss: 1.7541
[1m297/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.3239 - loss: 1.7394
[1m326/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.3267 - loss: 1.7263
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step - accuracy: 0.3269 - loss: 1.7254
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m8s[0m 12ms/step - accuracy: 0.3270 - loss: 1.7250 - val_accuracy: 0.4273 - val_loss: 1.2778
Epoch 2/25

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 23ms/step - accuracy: 0.4375 - loss: 1.2149
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[1m 58/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3789 - loss: 1.4258
[1m 89/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3852 - loss: 1.4204
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Epoch 3/25

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Epoch 4/25

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[1m230/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4470 - loss: 1.2699
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[1m288/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4505 - loss: 1.2658
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[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4524 - loss: 1.2633 - val_accuracy: 0.4947 - val_loss: 1.1756
Epoch 5/25

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[1m 31/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4827 - loss: 1.2051 
[1m 61/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4891 - loss: 1.1995
[1m 90/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4909 - loss: 1.1986
[1m123/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4915 - loss: 1.1985
[1m151/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4919 - loss: 1.1980
[1m182/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4919 - loss: 1.1980
[1m209/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4921 - loss: 1.1976
[1m239/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4922 - loss: 1.1970
[1m268/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4920 - loss: 1.1970
[1m294/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4915 - loss: 1.1971
[1m326/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.4912 - loss: 1.1971
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Epoch 6/25

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Epoch 7/25

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[1m290/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5228 - loss: 1.1128
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Epoch 8/25

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[1m119/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5611 - loss: 1.0474
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[1m268/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5588 - loss: 1.0531
[1m300/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5586 - loss: 1.0535
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Epoch 9/25

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[1m 30/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5993 - loss: 0.9690 
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[1m147/328[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5933 - loss: 0.9951
[1m177/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5917 - loss: 0.9972
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[1m234/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5889 - loss: 1.0012
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Epoch 10/25

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Epoch 11/25

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Epoch 12/25

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Epoch 13/25

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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m20s[0m 427ms/step2025-11-08 10:23:28.226127: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_141', 8 bytes spill stores, 8 bytes spill loads

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Saved model to disk.
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Loaded model from disk.
(1545, 6, 250)
(10469, 6, 250)

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7:00[0m 1s/step
[1m 60/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 855us/step
[1m120/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 849us/step
[1m175/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 872us/step
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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 16ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 18ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 19ms/step

[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 15ms/step
[1m62/89[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 825us/step
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step  
Global accuracy score (validation) = 54.81 [%]
Global F1 score (validation) = 53.68 [%]
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[[0.13345389 0.14204074 0.7085429  0.01596249]
 [0.41717625 0.43844104 0.10044958 0.04393306]
 [0.45742515 0.28734285 0.21127297 0.04395896]
 ...
 [0.31042704 0.09898393 0.35576054 0.23482853]
 [0.30410022 0.15977179 0.38980013 0.1463279 ]
 [0.2908754  0.07750068 0.15298896 0.47863495]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 68.91 [%]
Global accuracy score (test) = 54.37 [%]
Global F1 score (train) = 69.17 [%]
Global F1 score (test) = 54.55 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.42      0.38      0.40       400
MODERATE-INTENSITY       0.48      0.54      0.50       400
         SEDENTARY       0.59      0.70      0.64       400
VIGOROUS-INTENSITY       0.72      0.57      0.64       345

          accuracy                           0.54      1545
         macro avg       0.55      0.54      0.55      1545
      weighted avg       0.55      0.54      0.54      1545


Accuracy capturado en la ejecución 28: 54.37 [%]
F1-score capturado en la ejecución 28: 54.55 [%]

=== EJECUCIÓN 29 ===

--- TRAIN (ejecución 29) ---

--- TEST (ejecución 29) ---
2025-11-08 10:23:39.304431: 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 10:23:39.315814: 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 00:00:1762593819.329858  116282 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762593819.334192  116282 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762593819.346841  116282 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762593819.346862  116282 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762593819.346863  116282 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762593819.346864  116282 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-08 10:23:39.350223: 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 00:00:1762593821.673704  116282 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 1/25
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762593824.175785  116387 service.cc:152] XLA service 0x789704005250 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762593824.175810  116387 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-08 10:23:44.225088: 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 00:00:1762593824.536729  116387 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762593826.672159  116387 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m23:36[0m 4s/step - accuracy: 0.2812 - loss: 1.7924
[1m 24/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2457 - loss: 1.7994  
[1m 56/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2679 - loss: 1.7487
[1m 87/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2795 - loss: 1.7290
[1m118/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2881 - loss: 1.7123
[1m148/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2946 - loss: 1.6982
[1m178/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3007 - loss: 1.6840
[1m210/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3061 - loss: 1.6709
[1m240/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3106 - loss: 1.6601
[1m272/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3147 - loss: 1.6501
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Epoch 2/25

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Epoch 3/25

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Epoch 4/25

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[1m114/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4688 - loss: 1.2376
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Epoch 5/25

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[1m 28/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4625 - loss: 1.2476 
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[1m120/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4684 - loss: 1.2241
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[1m179/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4695 - loss: 1.2190
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[1m270/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4737 - loss: 1.2106
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Epoch 6/25

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[1m 29/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5257 - loss: 1.1425 
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[1m 88/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5210 - loss: 1.1517
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[1m181/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5157 - loss: 1.1565
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[1m239/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5140 - loss: 1.1571
[1m269/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5137 - loss: 1.1570
[1m300/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5138 - loss: 1.1564
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Epoch 7/25

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[1m 92/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5273 - loss: 1.1165
[1m124/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5282 - loss: 1.1174
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[1m245/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5317 - loss: 1.1172
[1m274/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5325 - loss: 1.1162
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[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5336 - loss: 1.1145 - val_accuracy: 0.5453 - val_loss: 1.0885
Epoch 8/25

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.6250 - loss: 0.8980
[1m 30/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5332 - loss: 1.0730 
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[1m 89/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5463 - loss: 1.0691
[1m118/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5501 - loss: 1.0656
[1m149/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5528 - loss: 1.0624
[1m177/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5540 - loss: 1.0610
[1m207/328[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5552 - loss: 1.0596
[1m235/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5561 - loss: 1.0587
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[1m295/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5576 - loss: 1.0576
[1m326/328[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.5582 - loss: 1.0572
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5582 - loss: 1.0572 - val_accuracy: 0.5530 - val_loss: 1.0696
Epoch 9/25

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.6875 - loss: 0.9568
[1m 30/328[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6025 - loss: 0.9967 
[1m 60/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5957 - loss: 1.0015
[1m 90/328[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5898 - loss: 1.0080
[1m123/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5852 - loss: 1.0126
[1m153/328[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5832 - loss: 1.0139
[1m184/328[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5818 - loss: 1.0148
[1m215/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5808 - loss: 1.0151
[1m247/328[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5806 - loss: 1.0147
[1m274/328[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5808 - loss: 1.0139
[1m303/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.5809 - loss: 1.0134
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5809 - loss: 1.0130
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Epoch 10/25

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Epoch 11/25

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Epoch 12/25

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[1m286/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6457 - loss: 0.8939
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Epoch 13/25

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Epoch 14/25

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Epoch 15/25

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[1m299/328[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.6966 - loss: 0.7875
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Epoch 16/25

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[1m48/49[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step   2025-11-08 10:24:02.765157: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_141', 8 bytes spill stores, 8 bytes spill loads

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Saved model to disk.
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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Loaded model from disk.
(1545, 6, 250)
(10469, 6, 250)

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7:03[0m 1s/step
[1m 57/328[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 903us/step
[1m116/328[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 875us/step
[1m174/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 875us/step
[1m230/328[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 880us/step
[1m289/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 875us/step
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step  
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m3s[0m 4ms/step

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

[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m58/89[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 880us/step
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 984us/step
Global accuracy score (validation) = 52.98 [%]
Global F1 score (validation) = 52.18 [%]
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[[0.20738976 0.10213964 0.57612365 0.11434694]
 [0.1707916  0.13399792 0.65075916 0.04445143]
 [0.50771636 0.2373568  0.2387828  0.01614412]
 ...
 [0.24943344 0.06461912 0.1807683  0.50517917]
 [0.15645824 0.05706882 0.07871205 0.70776093]
 [0.15564771 0.07519083 0.12056441 0.64859706]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 68.34 [%]
Global accuracy score (test) = 50.1 [%]
Global F1 score (train) = 68.25 [%]
Global F1 score (test) = 50.1 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.38      0.42      0.40       400
MODERATE-INTENSITY       0.44      0.35      0.39       400
         SEDENTARY       0.61      0.65      0.63       400
VIGOROUS-INTENSITY       0.58      0.60      0.59       345

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


Accuracy capturado en la ejecución 29: 50.1 [%]
F1-score capturado en la ejecución 29: 50.1 [%]

=== EJECUCIÓN 30 ===

--- TRAIN (ejecución 30) ---

--- TEST (ejecución 30) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
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Loaded model from disk.
(1545, 6, 250)
(10469, 6, 250)

[1m  1/328[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6:52[0m 1s/step
[1m 48/328[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step 
[1m109/328[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 932us/step
[1m168/328[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 908us/step
[1m229/328[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 888us/step
[1m291/328[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 872us/step
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step  
[1m328/328[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 4ms/step

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

[1m 1/89[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 15ms/step
[1m58/89[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 888us/step
[1m89/89[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step  
Global accuracy score (validation) = 54.67 [%]
Global F1 score (validation) = 54.21 [%]
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[[0.48101565 0.14203098 0.3286802  0.04827323]
 [0.20413882 0.10794901 0.5628018  0.12511034]
 [0.20214713 0.10982477 0.5321497  0.15587848]
 ...
 [0.00941452 0.00306266 0.01181121 0.9757116 ]
 [0.06275994 0.04543757 0.08906507 0.8027374 ]
 [0.11572573 0.08132697 0.08186768 0.72107965]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 71.0 [%]
Global accuracy score (test) = 49.45 [%]
Global F1 score (train) = 70.96 [%]
Global F1 score (test) = 50.02 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.35      0.39      0.37       400
MODERATE-INTENSITY       0.44      0.44      0.44       400
         SEDENTARY       0.66      0.60      0.63       400
VIGOROUS-INTENSITY       0.57      0.56      0.56       345

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


Accuracy capturado en la ejecución 30: 49.45 [%]
F1-score capturado en la ejecución 30: 50.02 [%]

=== RESUMEN FINAL ===
Accuracies: [51.13, 52.94, 53.92, 55.66, 52.82, 52.1, 49.45, 50.74, 50.68, 52.75, 52.36, 53.01, 51.78, 52.49, 53.2, 52.1, 52.69, 51.39, 50.87, 53.33, 52.62, 52.88, 52.36, 56.12, 50.87, 50.55, 52.3, 54.37, 50.1, 49.45]
F1-scores: [51.28, 52.32, 53.04, 55.36, 52.64, 52.33, 49.29, 50.87, 50.28, 52.44, 52.15, 53.27, 52.22, 53.08, 52.6, 51.91, 52.63, 51.22, 50.44, 54.09, 52.43, 53.58, 52.02, 55.38, 51.15, 49.59, 51.49, 54.55, 50.1, 50.02]
Accuracy mean: 52.2343 | std: 1.5535
F1 mean: 52.1257 | std: 1.5358

Resultados guardados en /mnt/nvme1n2/git/uniovi-simur-wearablepermed-data/output/Paper_results/cases_dataset_M/case_M_CAPTURE24_acc_gyr_superclasses_CPA_METs/metrics_test.npz
